text: 18 Pierre-Françcois Marteau, Saeid Soheily-Khah, and Nicolas Béchet 9 8 7 6 5 4 3 2 1 0 −0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 anomaly score for normal (blue) and anomaly (red) data Fig. 14. Anomaly scores when ’normal’ and ’anomaly’ data are mixed up in the train data used to build the IF or HIF. scores. If several distinct kind of anomalies exist, an iterative procedure can be set up to progressively isolate the anomalies according to their distinct patterns. 3.6 Complexity of the HIF algorithm Basically, the HIF algorithm has the same overall complexity than the IF algorithm, although some extra computational costs are involved during training and testing stages. ( · · ( )) ( · · ( )) IF has time complexities of in the training stage and in the testing stage. O t ψ loд ψ O n t loд ψ At training stage, the HIF algorithm requires to evaluate the centroids of the data attached to each of the external nodes, hence centroids in average need to be evaluated. The evaluation of a centroid is dependent on the number ψ of elements contained in the external buckets ( ). It seems difficult to estimate precisely the expectation of , neb neb nevertheless, Fig.15 presents the result of an empirical study that shows that if the maximal height of the iTree is ⌈ ( )⌉ lmax = loд2 ψ , then the average neb value increases slightly faster than a logarithmic law. For this test, the random ( ) (( ) ( )) forest has been build from a normally distributed dataset with 0 0 0 0 mean, and 3 0 0 3 covariance matrix. Hence, . , . , , , ( ( )) to maintain the overall algorithmic complexity at training stage close to one may increase slightly the maximal O ln ψ Manuscript submitted to ACM ##LTFigure## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine##

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text: Hybrid Isolation Forest - Application to Intrusion Detection 23 (a) (b) 25 30 25 20 20 15 15 10 10 5 5 0 0 −0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 −0.2 0.0 0.2 0.4 0.6 0.8 1.0 anomaly scores anomaly scores Fig. 17. Detection of anomalies in the ISCX HTTPWeb packet flows: score distributions for train ’normal’ data (blue), test ’normal’ data (green), and anomalies (red): (a) IF, (b) HIF. 1.0 0.8 0.6 0.4 IF HIF1 0.2 HIF2 1C-SVM 0.0 2C-SVM 0.0 0.2 0.4 0.6 0.8 1.0 Fig. 18. ROC curve obtained on the ISCX HTTPWeb subset for IF (dash-dotted, blue), HIF1 (solid, green), HIF2 (circle, red), 1C-SVM (dotted, purple), 2C-SVM (dotted, square, yellow) An exploratory data analysis based on the HIF score distributions can thus be used to set up efficiently an interactive semi-supervised procedure, in the scope of an active learning setting. Here again, according to Fig.19, adding very few labeled anomalies (as low as 2) into the HIF is enough to significantly improve the accuracy of the anomaly detection. This shows the supervised functionality of HIF can be particularly useful when some expertise is available. Manuscript submitted to ACM ##LTFigure## ##LTFigure## ##LTFigure## ##LTFigure## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine##

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text: anomaly

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text: HIF

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text: accuracy

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text: anomaly

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text: This

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text: supervised

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text: HIF

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text: particularly

text: useful

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text: Manuscript

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text: Bama,

text: M.S.Irfan

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text: and

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text: intrusion

text: detection

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text: International

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text: Computer

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text: Neurocomputing,

text: Advances

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text: artificial

text: neural

text: networks,

text: machine

text: learning,

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text: intelligence

text: (ESANN

text: Miguel

text: Á.

text: Carreira-perpiñán.

text: The

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text: dimensionality

text: reduction.

text: Proceedings

text: of

text: the

text: International

text: Conference

text: on

text: Machine

text: Learning

text: (ICML),

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text: Varun

text: Chandola,

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text: Vipin

text: Kumar.

text: Anomaly

text: detection:

text: A

text: survey.

text: ACM

text: Comput.

text: Surv.,

text: July

text: H.

text: Debar,

text: M.

text: Becker,

text: and

text: D.

text: Siboni.

text: A

text: neural

text: network

text: component

text: for

text: an

text: intrusion

text: detection

text: system.

text: In

text: Proceedings

text: IEEE

text: Computer

text: Society

text: Symposium

text: on

text: Research

text: in

text: Security

text: and

text: Privacy,

text: pages

text: Berkeley,

text: CA,

text: USA,

text: USENIX

text: Association.

text: Chesner

text: Désir,

text: Simon

text: Bernard,

text: Caroline

text: Petitjean,

text: and

text: Laurent

text: Heutte.

text: One

text: class

text: random

text: forests.

text: Pattern

text: Recogn.,

text: December

text: Zhiguo

text: Ding

text: and

text: Minrui

text: Fei.

text: An

text: anomaly

text: detection

text: approach

text: based

text: on

text: isolation

text: forest

text: algorithm

text: for

text: streaming

text: data

text: using

text: sliding

text: window.

text: IFAC

text: Proceedings

text: Volumes,

text: R.

text: Fujimaki.

text: Anomaly

text: detection

text: support

text: vector

text: machine

text: and

text: its

text: application

text: to

text: fault

text: diagnosis.

text: In

text: Eighth

text: IEEE

text: International

text: Conference

text: on

text: Data

text: Mining,

text: pages

text: Dec

text: Anup

text: K.

text: Ghosh

text: and

text: Aaron

text: Schwartzbard.

text: A

text: study

text: in

text: using

text: neural

text: networks

text: for

text: anomaly

text: and

text: misuse

text: detection.

text: In

text: Proceedings

text: of

text: the

text: Conference

text: on

text: USENIX

text: Security

text: Symposium

text: Volume

text: pages

text: Berkeley,

text: CA,

text: USA,

text: USENIX

text: Association.

text: Markus

text: Goldstein

text: and

text: Seiichi

text: Uchida.

text: A

text: comparative

text: evaluation

text: of

text: unsupervised

text: anomaly

text: detection

text: algorithms

text: for

text: multivariate

text: data.

text: PLOS

text: ONE,

text: Guofei

text: Gu,

text: Prahlad

text: Fogla,

text: David

text: Dagon,

text: Wenke

text: Lee,

text: and

text: Boris

text: Škorić.

text: Towards

text: an

text: information-theoretic

text: framework

text: for

text: analyzing

text: intrusion

text: detection

text: systems.

text: In

text: European

text: Symposium

text: on

text: Research

text: in

text: Computer

text: Security

text: (ESORICS),

text: Hamburg,

text: pages

text: Springer,

text: Sep

text: LNCS

text: Vol.

text: Carlo

text: Cattani

text: Jianwei

text: Zheng,

text: Hangke

text: Zhang

text: and

text: Wanliang

text: Wang.

text: Dimensionality

text: reduction

text: by

text: supervised

text: neighbor

text: embedding

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text: laplacian

text: search.

text: Computational

text: and

text: Mathematical

text: Methods

text: in

text: Medicine,

text: Christian

text: Jutten

text: and

text: Jeanny

text: Herault.

text: Blind

text: separation

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text: sources,

text: part

text: An

text: adaptive

text: algorithm

text: based

text: on

text: neuromimetic

text: architecture.

text: Signal

text: Process.,

text: August

text: Michael

text: Kemmler,

text: Erik

text: Rodner,

text: Esther-Sabrina

text: Wacker,

text: and

text: Joachim

text: Denzler.

text: One-class

text: classification

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text: processes.

text: Pattern

text: Recognition,

text: Wenke

text: Lee

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text: Dong

text: Xiang.

text: Information-theoretic

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text: detection.

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text: Security

text: and

text: Privacy,

text: SP

text: pages

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text: DC,

text: USA,

text: IEEE

text: Computer

text: Society.

text: Wei-Chao

text: Lin,

text: Shih-Wen

text: Ke,

text: and

text: Chih-Fong

text: Tsai.

text: Cann:

text: An

text: intrusion

text: detection

text: system

text: based

text: on

text: combining

text: cluster

text: centers

text: and

text: nearest

text: neighbors.

text: Knowledge-Based

text: Systems,

text: Ying-Dar

text: Lin,

text: Po-Ching

text: Lin,

text: Sheng-Hao

text: Wang,

text: I-Wei

text: Chen,

text: and

text: Yuan-Cheng

text: Lai.

text: Pcaplib:

text: A

text: system

text: of

text: extracting,

text: classifying,

text: and

text: anonymizing

text: real

text: packet

text: traces.

text: IEEE

text: Systems

text: Journal,

text: F.

text: T.

text: Liu,

text: K.

text: M.

text: Ting,

text: and

text: Z.

text: H.

text: Zhou.

text: Isolation

text: forest.

text: In

text: Proceedings

text: of

text: the

text: IEEE

text: International

text: Conference

text: on

text: Data

text: Mining

text: pages

text: Fei

text: Tony

text: Liu,

text: Kai

text: Ming

text: Ting,

text: and

text: Zhi-Hua

text: Zhou.

text: Isolation-based

text: anomaly

text: detection.

text: ACM

text: Trans.

text: Knowl.

text: Discov.

text: Data,

text: march

text: Dalton

text: Lunga

text: and

text: Okan

text: K.

text: Ersoy.

text: Spherical

text: stochastic

text: neighbor

text: embedding

text: of

text: hyperspectral

text: data.

text: IEEE

text: Trans.

text: Geoscience

text: and

text: Remote

text: Sensing,

text: Bruno

text: Richard

text: Preiss.

text: Data

text: Structures

text: and

text: Algorithms

text: with

text: Object-Oriented

text: Design

text: Patterns

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text: Java.

text: wiley,

text: pp.

text: ISBN

text: Jake

text: Ryan,

text: Meng-Jang

text: Lin,

text: and

text: Risto

text: Miikkulainen.

text: Intrusion

text: detection

text: with

text: neural

text: networks.

text: In

text: Michael

text: I.

text: Jordan,

text: Michael

text: J.

text: Kearns,

text: and

text: Sara

text: A.

text: Solla,

text: editors,

text: Advances

text: in

text: Neural

text: Information

text: Processing

text: Systems

text: pages

text: Cambridge,

text: MA:

text: MIT

text: Press,

text: Bernhard

text: Schölkopf,

text: Alexander

text: Smola,

text: and

text: Klaus-Robert

text: Müller.

text: Nonlinear

text: component

text: analysis

text: as

text: a

text: kernel

text: eigenvalue

text: problem.

text: Neural

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text: Yanhui

text: Shen,

text: Huawen

text: Liu,

text: Yanxia

text: Wang,

text: Zhongyu

text: Chen,

text: and

text: Guanghua

text: Sun.

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text: Novel

text: Isolation-Based

text: Outlier

text: Detection

text: Method,

text: pages

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text: Shiravi,

text: Hadi

text: Shiravi,

text: Mahbod

text: Tavallaee,

text: and

text: Ali

text: A.

text: Ghorbani.

text: Toward

text: developing

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text: systematic

text: approach

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text: Computer

text: Security,

text: May

text: Manuscript

text: submitted

text: to

text: ACM


text: 10 Pierre-Françcois Marteau, Saeid Soheily-Khah, and Nicolas Béchet Algorithm 5: Function computeAnomalyCentroid( ) T Input : : a node in an iTree T Output:none 1 if T is an external node then =None 2 Ca | | ≥ if 0 then 3 T.Xa =Centroid( ) 4 Ca T.Xa end 5 end 6 else 7 computeAnomalyCentroid( ) 8 T.lef t computeAnomalyCentroid( ) 9 T.riдht end 10 ( ) Algorithm 6: Function hiScore x,T,e Input : an instance, an hiTree, the current path length; to be initialized to zero when first called x T e ( ) ( ) Output: , the path length for , , the Euclidean distance between and the centroid associated to the h x x δ x x ( ) external node, and the minimal Euclidean distance between and the centroid of the anomalies δa x x attached to , T T.Xa 1 if T i(s a)n extern(al node)th(en) 2 h x = e + c T.size (c . is defined in Equation (1)) ( ) ( ) = EuclideanDistance 3 δ x( ) x(,T.CS ) = EuclideanDistance 4 δa x x,T.Ca ( ) ( ) ( ) return , , 5 h x δ x δa x end 6 ← 7 a T.splitAtt [ ] if then 8 x a < T.splitVa(lue ) 9 return hiScore x,T.lef t,e + 1 end 10 else 11 ( ) 12 return hiScore x,T.riдht,e + 1 end 13 ( ) ( ) The IF score for data and dataset size , given in Eq.(2) need to be aggregated with the two new scores s x,n x n sc x ( ) and . We adopt a simple aggregation in two steps: sa x [ ] (1) first, all the three scores are normalized such as to fit into the unit interval 0;1 . On the train data we evaluate ( ) ∈ { ( ) ( ) ( )} for s x s x,n ,sc x ,sa x ( ) − ( ( )) s˜(x) = s (x( ))m−in s x( ( )) (5) max s x min s x (cid:16) ∈ [ ] (cid:17) (2) then we introduce two meta-parameters and 0;1 and evaluate the following linear model: α1 α2 ( ) · · ( ) ( − ) · ( ) ( − ) · ( )) shif x,n = α2 α1 s˜ x,n + 1 α1 s˜c x + 1 α2 s˜a x (6) Manuscript submitted to ACM ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine##

text: Pierre-Françcois

text: Marteau,

text: Saeid

text: Soheily-Khah,

text: and

text: Nicolas

text: Béchet

text: Algorithm

text: Function

text: computeAnomalyCentroid(

text: T

font: GVLRLE+LinLibertineI

text: Input

text: a

text: node

text: in

text: an

text: iTree

text: T

text: Output:none

text: if

text: T

text: is

text: an

text: external

text: node

text: then

text: =None

text: Ca

font: RJGYGT+txsys

text: if

text: then

text: T.Xa

text: =Centroid(

text: Ca

text: T.Xa

text: end

text: end

text: else

text: computeAnomalyCentroid(

text: T.lef

text: t

text: computeAnomalyCentroid(

text: T.riдht

text: end

text: Algorithm

text: Function

text: hiScore

text: x,T,e

text: Input

text: an

text: instance,

text: an

text: hiTree,

text: the

text: current

text: path

text: length;

text: to

text: be

text: initialized

text: to

text: zero

text: when

text: first

text: called

text: x

text: T

text: e

text: Output:

text: the

text: path

text: length

text: for

text: the

text: Euclidean

text: distance

text: between

text: and

text: the

text: centroid

text: associated

text: to

text: the

text: h

text: x

text: x

text: δ

text: x

text: x

text: external

text: node,

text: and

text: the

text: minimal

text: Euclidean

text: distance

text: between

text: and

text: the

text: centroid

text: of

text: the

text: anomalies

text: δa

text: x

text: x

text: attached

text: to

text: T

text: T.Xa

text: if

text: T

text: i(s

text: a)n

text: extern(al

text: node)th(en)

text: h

text: x

text: e

text: c

text: T.size

text: (c

text: is

text: defined

text: in

text: Equation

text: EuclideanDistance

text: δ

text: x(

text: x(,T.CS

text: EuclideanDistance

text: δa

text: x

text: x,T.Ca

text: return

text: h

text: x

text: δ

text: x

text: δa

text: x

text: end

text: a

text: T.splitAtt

text: if

text: then

text: x

text: a

text: T.splitVa(lue

text: return

text: hiScore

text: x,T.lef

text: t,e

text: end

text: else

text: return

text: hiScore

text: x,T.riдht,e

text: end

text: The

text: IF

text: score

text: for

text: data

text: and

text: dataset

text: size

text: given

text: in

text: need

text: to

text: be

text: aggregated

text: with

text: the

text: two

text: new

text: scores

text: s

text: x,n

text: x

text: n

text: sc

text: x

text: and

text: We

text: adopt

text: a

text: simple

text: aggregation

text: in

text: two

text: steps:

text: sa

text: x

text: first,

text: all

text: the

text: three

text: scores

text: are

text: normalized

text: such

text: as

text: to

text: fit

text: into

text: the

text: unit

text: interval

text: On

text: the

text: train

text: data

text: we

text: evaluate

text: for

text: s

text: x

text: s

text: x,n

text: ,sc

text: x

text: ,sa

text: x

text: s˜(x)

text: s

text: (x(

text: ))m−in

text: s

text: x(

text: max

text: s

text: x

text: min

text: s

text: x

text: then

text: we

text: introduce

text: two

text: meta-parameters

text: and

text: and

text: evaluate

text: the

text: following

text: linear

text: model:

text: α1

text: α2

text: shif

text: x,n

text: α2

text: α1

text: s˜

text: x,n

text: α1

text: s˜c

text: x

text: α2

text: s˜a

text: x

text: Manuscript

text: submitted

text: to

text: ACM

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##

text: ##LTLine##


text: G G Thus, by Poisson-thinning, coupled versions of l , can be obtained by remov- G N N ing all arcs in with both endpoints in V. Clearly, the degrees of v(1), . . . , v(k) in G N → → l are independent. Now, since ∑ ∈ \ W(in)/L 1 and ∑ ∈ \ W(out)/L 1 N v [N] V Gv N v [N] V Gv N by (2), the limiting degree distribution in l is as stated. To deﬁne u , let M = { ∈ } − N N max W(in), W(out); v V and sample k(k 1) i.i.d. Poisson(M2/L )-distributed random v v (cid:54) G N variables F(v(i), v(j)), i = j = 1, . . . , k. Let u be given by the edge variables N { ∈ } Eu(v, w) = El(v, w) + 1l v, w V F(v, w). G G G ≥ G G G Then clearly, u and can be coupled, such that u . But u and l only differ N − N N N N N by a Poisson(k(k 1)M2/L )-distributed number of edges and this difference converges W N G to 0 for almost every . Hence the degrees in u are asymptotically equal to the degrees G N in l . N If ρ is ﬁnite then the model produces at most a ﬁnite number of loops. G Proposition 9 (Finite number of loops). If ρ < ∞, then total number of loops in N is asymptotically distributed as Poisson(ρ/µ). Proof. By deﬁnition of the model, ist is clear that under PW, the loop numbers E(1, 1), . . . , E(N, N) are independent Poisson(W(in)W(out)/L )-distributed random variables. Thus their sum v v N (cid:16) (cid:17) is Poisson with parameter ∑N W(in)W(out) N ∑N W(in)W(out) v v v v = . L L N v=1 N N v=1 The term in brackets PW-almost surely converges to ρ by the strong law of large numbers. The factor N/L satisﬁes N N N2 − LN = (µ + oP(1))∑v,w Wv(in)Ww(out) = (1 + oP(1))µ 1 by (2) and the strong law of large numbers. By Lemma 7, it follows that the limiting distributions is Poisson(ρ/µ). Finally, as a corollary of the conditional results, we obtain a characterisation of the limiting distribution of the in- and outdegree of a typical vertex under P. ∈ Corollary 10 (Limiting distribution of vertex degrees). For U(N) [N] uniformly chosen G W denote by D(N) = (D(in)(N), D(out)(N)) its joint in- and outdegree in N( ). Then → → D(N) D in distribution as N ∞, where D is bivariate mixed Poisson with mixing distribution W, i.e. conditionally on the mixing variable W = (W(in), W(out)), ∼ ⊗ D Poisson(W(in)) Poisson(W(out)). 10 ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine##

text: G

text: G

text: Thus,

font: BHCZPP+URWPalladioL-Roma

text: by

text: Poisson-thinning,

text: coupled

text: versions

text: of

text: l

font: CEEIYM+URWPalladioL-Ital

text: can

text: be

text: obtained

text: by

text: remov-

text: G

text: N

text: N

text: ing

text: all

text: arcs

text: in

text: with

text: both

text: endpoints

text: in

text: V.

text: Clearly,

text: the

text: degrees

text: of

text: v(k)

text: in

text: G

text: N

text: l

text: are

text: independent.

text: Now,

text: since

font: AOIEVJ+PazoMath

text: W(in)/L

text: and

text: W(out)/L

text: N

text: v

text: [N]

text: V

text: Gv

text: N

text: v

text: [N]

text: V

text: Gv

text: N

text: by

text: the

text: limiting

text: degree

text: distribution

text: in

text: l

text: is

text: as

text: stated.

text: To

text: deﬁne

text: u

text: let

text: M

text: N

text: N

text: max

text: W(in),

text: W(out);

text: v

text: V

text: and

text: sample

text: k(k

text: i.i.d.

text: )-distributed

text: random

text: v

text: v

text: G

text: N

text: variables

text: F(v(i),

text: v(j)),

text: i

text: j

text: k.

text: Let

text: u

text: be

text: given

text: by

text: the

text: edge

text: variables

text: N

text: Eu(v,

text: w)

text: El(v,

text: w)

text: v,

text: w

text: V

text: F(v,

text: w).

text: G

text: G

text: G

text: G

text: G

text: G

text: Then

text: clearly,

text: u

text: and

text: can

text: be

text: coupled,

text: such

text: that

text: u

text: But

text: u

text: and

text: l

text: only

text: differ

text: N

text: N

text: N

text: N

text: N

text: N

text: by

text: a

text: Poisson(k(k

text: )-distributed

text: number

text: of

text: edges

text: and

text: this

text: difference

text: converges

text: W

text: N

text: G

text: to

text: for

text: almost

text: every

text: Hence

text: the

text: degrees

text: in

text: u

text: are

text: asymptotically

text: equal

text: to

text: the

text: degrees

text: G

text: N

text: in

text: l

text: N

text: If

text: ρ

text: is

text: ﬁnite

text: then

text: the

text: model

text: produces

text: at

text: most

text: a

text: ﬁnite

text: number

text: of

text: loops.

text: G

text: Proposition

text: (Finite

text: number

text: of

text: loops).

text: If

text: ρ

text: then

text: total

text: number

text: of

text: loops

text: in

text: N

text: is

text: asymptotically

text: distributed

text: as

text: Poisson(ρ/µ).

text: Proof.

text: By

text: deﬁnition

text: of

text: the

text: model,

text: ist

text: is

text: clear

text: that

text: under

text: PW,

text: the

text: loop

text: numbers

text: E(N,

text: N)

text: are

text: independent

text: Poisson(W(in)W(out)/L

text: )-distributed

text: random

text: variables.

text: Thus

text: their

text: sum

text: v

text: v

text: N

text: is

text: Poisson

text: with

text: parameter

text: ∑N

text: W(in)W(out)

text: N

text: ∑N

text: W(in)W(out)

text: v

text: v

text: v

text: v

text: L

text: L

text: N

text: N

text: N

text: The

text: term

text: in

text: brackets

text: PW-almost

text: surely

text: converges

text: to

text: ρ

text: by

text: the

text: strong

text: law

text: of

text: large

text: numbers.

text: The

text: factor

text: N/L

text: satisﬁes

text: N

text: N

text: LN

text: (µ

text: Wv(in)Ww(out)

text: by

text: and

text: the

text: strong

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text: with the ﬁrst sentence of the corresponding article deﬁned as the ratio of the number of word types in Wikipedia. Thus in this data-set, texts and data to the number of words in a text. To address the (infoboxes) were authored by Wikipedia contribu- fact that this ratio tends to decrease with the size of tors. As the data-set is much larger than the other the corpus, Mean segmental TTR (MSTTR) is com- data-sets and is not domain speciﬁc, we extract three puted by dividing the corpus into successive seg- subsets of it for better comparison: two whose size ments of a given length and then calculating the av- is similar to the other data-sets (WIKIBIO16317, WIK- erage TTR of all segments. IBIO2647) and one which is domain speciﬁc in that all Overall, the WIKIBIO data-set, even when restricted biographies are about astronauts (WIKIBIOASTRO). to a single type of entity (namely, astronauts) has The other two data-sets were created man- a higher MSTTR. This higher lexical variation is ually with humans providing text for dialogue probably due to the fact that this data-set also has acts in the case of (Wen et al., 2015; Wen the highest number of attributes (cf. Table 1): more et al., 2016)’s data-sets (RNNLG , RNNLG , attributes brings more diversity and thus better lex- Laptop TV RNNLG , RNNLG ) and producing image ical range. And indeed, there is a positive correla- Hotel Restaurant descriptions in the case of (Novikova and Rieser, tion between the number of attributes in the data- 2016)’s data-set (IMAGEDESC). set and MSTTR (Spearman’s rank correlation coef- We also include a text-only corpus for compar- ﬁcient rho = +0.385). ison with the texts contained in our three data-sets. Lexical sophistication, also known as lexical This corpus (GMB) consists of the texts contained the rareness, measures the proportion of relatively un- Groningen Meaning Bank (Version 1.0.0, (Basile et usual or advanced word types in the text. In practice, al., 2012)) and covers different genres (e.g., news, LS is the proportion of lexical word types (lemma) jokes, fables). which are not in the list of 2,000 most frequent Linguistic and Computational Adequacy Ta- words generated from the British National Corpus. ble 1 gives some descriptive statistics for each of Again the WIKIBIO data-set has a markedly higher these three data-sets. It shows marked differences level of lexical sophistication than the other data- in terms of size ( WIKIBIO16317 being the largest and sets. This might be because the WIKIBIO text are IMAGEDESC the smallest), number of distinct relations not crowd-sourced but edited independently of input (from 16 for IMAGEDESC to 2367 for WIKIBIO16317 ) data thereby leaving more freedom to the authors to and average number of paraphrases (15.11 for IM- include additional information. It may also result AGEDESC against 1 to 3.72 for the other two data-sets). from the fact that the WIKIBIO data-set, even though The number of distinct meaning representations (se- it is restricted to biographies, covers a much more mantic variability) also varies widely (from 77 dis- varied set of domains than the other data-sets as peo- tinct MRs for the IMAGEDESC corpus to 12527 for ple’s lives may be very diverse and consequently, a RNNLG ). Overall though, the main observation more varied range of topics may be mentioned than Laptop is that the number of distinct attributes is relatively in a domain restricted data-set. small and that the high number of distinct meaning Syntactic Diversity To support the training of sur- representations essentially stems from various com- face realisers with wide syntactic coverage, a bench- binations of a restricted number of attributes. mark needs to show a balanced distribution of the Lexical Richness To assess the extent to which various syntactic phenomena present in the target language. To compare the syntactic coverage of the three data-sets support the training of lexically the three data-sets, we use the system for automatic rich surface realisers, we used the Lexical Complex- measurement of syntactic complexity developed by ity Analyser developed by (Lu, 2012), a system de- (Lu, 2010). Brieﬂy, this system decomposes parse signed to automate the measurement of various di- trees1 into component sub-trees and scores each of mensions of lexical richness. These include lexical these sub-trees based on the type of the syntactic sophistication (LS) and mean segmental type-token ratio (MSTTR). Table 2 summarises the results. 1Parses are obtained using Collins’ constituent parser Type-token ratio (TTR) is a measure of diversity (Collins, 1999). ##LTLine##

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text: ##LTLine##


text: M. Livesu, S. Ellero, J. Martínez, S. Lefebvre & M. Attene / From 3D Models to 3D Prints : an Overview of the Processing Pipeline decomposed into a set of monotone polygons; [DPCL14] uses con- would be fused together during the printing process (e.g. this ∗ vex decomposition; [ZCG 16] uses the iso-contours of an inward might happen for bearing spheres); • distance ﬁeld from the border; [HLA94] uses a Voronoi-based ap- The model has none of the aforementioned issues, but printing proach and [KP98] uses a medial axis based approach. the assembly would require the insertion of support structures that could not be removed; • 3.5.1.5. Performances Besides the method used to decompose The model has none of the aforementioned issues, but printing the domain and the particular curves used to ﬁll each pocket, an- the assembly would require the insertion of support structures other important factor in the deﬁnition of an efﬁcient toolpath is that, after removal, would produce rough surfaces (above the tol- the reduction of the so called machine airtime, that is, the time nec- erances set in the design). essary to move the nozzle (or the laser) from the end of a curve to For all these reasons, the current practice for the manufacturing the beginning of the subsequent one. Given a toolpath composed of components made of multiple parts consists in building each part of a set of disconnected curves, the machine airtime can be mini- separately, and thus requires a reassembly of the physical objects mized by acting on to three separate variables: the order in which to form the ultimate component. Some elements of the design (e.g. the curves are processed, the path that takes the nozzle from the end screws, bolts, bearings, ...) are bought from external producers that of a curve to the beginning of the next one, and the orientation of offer a catalogue of standardized components, whereas all the re- each curve (from begin to end, or from end to begin). For the lat- maining parts are produced by additive manufacturing. Typically, ter, notice that for closed loops, the machine may chose any point each part undergoes most of the process planning steps indepen- within the loop as a starting point. The path planning optimization dently. Each so-prepared part (that is, checked, oriented, and sup- problem has been shown to be related to the Travelling Salesman ported) is sent to the software that drives the printer and, through Problem (TSP), which is NP-complete. Many methods have been this software, an operator places the part in a free portion of the proposed for its approximate solution, using genetic algorithms building plate. When the plate is full, or when there are no more [Wei09,WMJC02], the Christoﬁdes algorithm [FGCC16,GCFC16] parts to print, the operator runs the actual printing process. In this or other heuristics [CDW03]. case, the only process planning steps which are common to all the parts are the slicing and the creation of the toolpath. It is also possi- 3.5.2. Raster case ble to slice one part at a time while using the same layer thickness: this is done, for example, on EOS machines which use the PSW Recent printing technologies such as Z-Corp. and the DLP/SLA software to place pre-sliced parts on the building platform. Algo- printers (i.e. stereolithography printers that exploit the Digital Light rithmic approaches exist that try to optimize for both quality and Processing technology) treat each slice as an array of pixels. For packing efﬁciency by properly orienting and placing the various the Z-Corp. case the machine toolpath consists in a trivial visit of a parts on the platform [CDMS06,ZBHK15]. regular 2D grid. From a machine usage point of view an important parameter is the binder saturation, which affects both the strength 3.6.1. Printing big objects and the accuracy of a printed part [VC11]. Typically, a binder is ﬁrst applied with a higher saturation to the edges of the part, creat- When even a single object is larger than the printing chamber, ing a strong shell for the exterior. Next, an infrastructure is created solutions exist to split it into parts to be printed independently for the part walls, which are also built with a higher saturation. and reassembled afterwards. A noticeable example is given in The remaining interior areas are printed with a lower binder satura- [LBRM12], where the algorithm decomposes a 3D object into re- tion, which gives the part its stability [ZCo07]. For the DLP-based assemblable parts, each contained within a given printing volume. printers there is no notion of machine toolpath, because the whole Besides reassemblability, this algorithm strives to avoid the pro- image/slice is projected onto the ﬁrst layer of the resin tank. In duction of too small parts, and considers the structural soundness this case it is important to project each slice for the proper amount by avoiding to put seams in areas of high mechanical stress (see of time (exposure time), in order to balance surface accuracy with Figure 17). In a similar work [HFW11], the seams are placed along part strength. Exposure time depends on material, layer thickness lines of high curvature so as to minimize their impact on the aes- and slice resolution. Notice that commercial slicers allow to export thetics. In both [LBRM12] and [HFW11], connectors are created to vector slices (e.g. in SVG format) even for raster printers [Sli11]. ease the actual reassembly. Conversely, instead of using connectors, This is because slice rescaling is usually performed internally prior glue, or screws, in [SFLF15] the subdivision strives to create a part to projection, and scaling a raster image may introduce unnecessary conﬁguration that allows self-interlocking, such that the assembled artifacts such as image blurring. object can be not only repeatedly disassembled and reassembled, but also strongly connected by the parts’ own geometry. 3.6. Multiple components / Batch printing 3.6.2. Print, pack and ship paradigm In principle, a 3D model made of multiple parts can be printed all Delivering a printed object (e.g. to a customer) has a cost that at once, that is, pre-assembled. However, with current technologies grows as the size of the pack grows. Thus, it is important to in- this approach might still have several drawbacks: vestigate how a 3D model can be split into easily printable parts • The pre-assembled model can be too large to ﬁt the printing that can eventually be tightly packed in a box and reassembled chamber, whereas each single constituting part would ﬁt; at the destination. In [Att15], an algorithm that performs this kind • The model is small enough, but it contains tangency parts which of split is proposed based on a user-controllable tradeoff between (cid:13) c 2017TheAuthor(s) (cid:13) ComputerGraphicsForum c 2017TheEurographicsAssociationandJohnWiley&SonsLtd.

text: M.

text: Livesu,

text: S.

text: Ellero,

text: J.

text: Martínez,

text: S.

text: Lefebvre

text: M.

text: Attene

text: From

text: Models

text: to

text: Prints

text: an

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text: of

text: the

text: Processing

text: Pipeline

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text: into

text: a

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text: of

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text: polygons;

text: uses

text: con-

text: would

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text: during

text: the

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text: (e.g.

text: this

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text: bearing

text: spheres);

text: distance

text: ﬁeld

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text: the

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text: uses

text: a

text: Voronoi-based

text: ap-

text: The

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text: has

text: of

text: the

text: aforementioned

text: issues,

text: but

text: printing

text: proach

text: and

text: uses

text: a

text: medial

text: axis

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text: approach.

text: the

text: assembly

text: would

text: require

text: the

text: insertion

text: of

text: support

text: structures

text: that

text: could

text: not

text: be

text: removed;

text: Performances

text: Besides

text: the

text: method

text: used

text: to

text: decompose

text: The

text: model

text: has

text: of

text: the

text: aforementioned

text: issues,

text: but

text: printing

text: the

text: domain

text: and

text: the

text: particular

text: curves

text: used

text: to

text: ﬁll

text: each

text: pocket,

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text: important

text: factor

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text: the

text: deﬁnition

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text: essary

text: to

text: move

text: the

text: nozzle

text: (or

text: the

text: laser)

text: from

text: the

text: end

text: of

text: a

text: curve

text: to

text: For

text: all

text: these

text: reasons,

text: the

text: current

text: practice

text: for

text: the

text: manufacturing

text: the

text: beginning

text: of

text: the

text: subsequent

text: one.

text: Given

text: a

text: toolpath

text: composed

text: of

text: components

text: made

text: of

text: multiple

text: parts

text: consists

text: in

text: building

text: each

text: part

text: of

text: a

text: set

text: of

text: disconnected

text: curves,

text: the

text: machine

text: airtime

text: can

text: be

text: mini-

text: separately,

text: and

text: thus

text: requires

text: a

text: reassembly

text: of

text: the

text: physical

text: objects

text: mized

text: by

text: acting

text: on

text: to

text: three

text: separate

text: variables:

text: the

text: order

text: in

text: which

text: to

text: form

text: the

text: ultimate

text: component.

text: Some

text: elements

text: of

text: the

text: design

text: (e.g.

text: the

text: curves

text: are

text: processed,

text: the

text: path

text: that

text: takes

text: the

text: nozzle

text: from

text: the

text: end

text: screws,

text: bolts,

text: bearings,

text: are

text: bought

text: from

text: external

text: producers

text: that

text: of

text: a

text: curve

text: to

text: the

text: beginning

text: of

text: the

text: next

text: one,

text: and

text: the

text: orientation

text: of

text: offer

text: a

text: catalogue

text: of

text: standardized

text: components,

text: whereas

text: all

text: the

text: re-

text: each

text: curve

text: (from

text: begin

text: to

text: end,

text: or

text: from

text: end

text: to

text: begin).

text: For

text: the

text: lat-

text: maining

text: parts

text: are

text: produced

text: by

text: additive

text: manufacturing.

text: Typically,

text: ter,

text: notice

text: that

text: for

text: closed

text: loops,

text: the

text: machine

text: may

text: chose

text: any

text: point

text: each

text: part

text: undergoes

text: most

text: of

text: the

text: process

text: planning

text: steps

text: indepen-

text: within

text: the

text: loop

text: as

text: a

text: starting

text: point.

text: The

text: path

text: planning

text: optimization

text: dently.

text: Each

text: so-prepared

text: part

text: (that

text: is,

text: checked,

text: oriented,

text: and

text: sup-

text: problem

text: has

text: been

text: shown

text: to

text: be

text: related

text: to

text: the

text: Travelling

text: Salesman

text: ported)

text: is

text: sent

text: to

text: the

text: software

text: that

text: drives

text: the

text: printer

text: and,

text: through

text: Problem

text: (TSP),

text: which

text: is

text: NP-complete.

text: Many

text: methods

text: have

text: been

text: this

text: software,

text: an

text: operator

text: places

text: the

text: part

text: in

text: a

text: free

text: portion

text: of

text: the

text: proposed

text: for

text: its

text: approximate

text: solution,

text: using

text: genetic

text: algorithms

text: building

text: plate.

text: When

text: the

text: plate

text: is

text: full,

text: or

text: when

text: there

text: are

text: no

text: more

text: the

text: Christoﬁdes

text: algorithm

text: parts

text: to

text: print,

text: the

text: operator

text: runs

text: the

text: actual

text: printing

text: process.

text: In

text: this

text: or

text: other

text: heuristics

text: case,

text: the

text: only

text: process

text: planning

text: steps

text: which

text: are

text: common

text: to

text: all

text: the

text: parts

text: are

text: the

text: slicing

text: and

text: the

text: creation

text: of

text: the

text: toolpath.

text: It

text: is

text: also

text: possi-

text: Raster

text: case

text: ble

text: to

text: slice

text: one

text: part

text: at

text: a

text: time

text: while

text: using

text: the

text: same

text: layer

text: thickness:

text: this

text: is

text: done,

text: for

text: example,

text: on

text: EOS

text: machines

text: which

text: use

text: the

text: PSW

text: Recent

text: printing

text: technologies

text: such

text: as

text: Z-Corp.

text: and

text: the

text: DLP/SLA

text: software

text: to

text: place

text: pre-sliced

text: parts

text: on

text: the

text: building

text: platform.

text: Algo-

text: printers

text: (i.e.

text: stereolithography

text: printers

text: that

text: exploit

text: the

text: Digital

text: Light

text: rithmic

text: approaches

text: exist

text: that

text: try

text: to

text: optimize

text: for

text: both

text: quality

text: and

text: Processing

text: technology)

text: treat

text: each

text: slice

text: as

text: an

text: array

text: of

text: pixels.

text: For

text: packing

text: efﬁciency

text: by

text: properly

text: orienting

text: and

text: placing

text: the

text: various

text: the

text: Z-Corp.

text: case

text: the

text: machine

text: toolpath

text: consists

text: in

text: a

text: trivial

text: visit

text: of

text: a

text: parts

text: on

text: the

text: platform

text: regular

text: grid.

text: From

text: a

text: machine

text: usage

text: point

text: of

text: view

text: an

text: important

text: parameter

text: is

text: the

text: binder

text: saturation,

text: which

text: affects

text: both

text: the

text: strength

text: Printing

text: big

text: objects

text: and

text: the

text: accuracy

text: of

text: a

text: printed

text: part

text: Typically,

text: a

text: binder

text: is

text: ﬁrst

text: applied

text: with

text: a

text: higher

text: saturation

text: to

text: the

text: edges

text: of

text: the

text: part,

text: creat-

text: When

text: even

text: a

text: single

text: object

text: is

text: larger

text: than

text: the

text: printing

text: chamber,

text: ing

text: a

text: strong

text: shell

text: for

text: the

text: exterior.

text: Next,

text: an

text: infrastructure

text: is

text: created

text: solutions

text: exist

text: to

text: split

text: it

text: into

text: parts

text: to

text: be

text: printed

text: independently

text: for

text: the

text: part

text: walls,

text: which

text: are

text: also

text: built

text: with

text: a

text: higher

text: saturation.

text: and

text: reassembled

text: afterwards.

text: A

text: noticeable

text: example

text: is

text: given

text: in

text: The

text: remaining

text: interior

text: areas

text: are

text: printed

text: with

text: a

text: lower

text: binder

text: satura-

text: where

text: the

text: algorithm

text: decomposes

text: a

text: object

text: into

text: re-

text: tion,

text: which

text: gives

text: the

text: part

text: its

text: stability

text: For

text: the

text: DLP-based

text: assemblable

text: parts,

text: each

text: contained

text: within

text: a

text: given

text: printing

text: volume.

text: printers

text: there

text: is

text: no

text: notion

text: of

text: machine

text: toolpath,

text: because

text: the

text: whole

text: Besides

text: reassemblability,

text: this

text: algorithm

text: strives

text: to

text: avoid

text: the

text: pro-

text: image/slice

text: is

text: projected

text: onto

text: the

text: ﬁrst

text: layer

text: of

text: the

text: resin

text: tank.

text: In

text: duction

text: of

text: too

text: small

text: parts,

text: and

text: considers

text: the

text: structural

text: soundness

text: this

text: case

text: it

text: is

text: important

text: to

text: project

text: each

text: slice

text: for

text: the

text: proper

text: amount

text: by

text: avoiding

text: to

text: put

text: seams

text: in

text: areas

text: of

text: high

text: mechanical

text: stress

text: (see

text: of

text: time

text: (exposure

text: time),

text: in

text: order

text: to

text: balance

text: surface

text: accuracy

text: with

text: Figure

text: In

text: a

text: similar

text: work

text: the

text: seams

text: are

text: placed

text: along

text: part

text: strength.

text: Exposure

text: time

text: depends

text: on

text: material,

text: layer

text: thickness

text: lines

text: of

text: high

text: curvature

text: so

text: as

text: to

text: minimize

text: their

text: impact

text: on

text: the

text: aes-

text: and

text: slice

text: resolution.

text: Notice

text: that

text: commercial

text: slicers

text: allow

text: to

text: export

text: thetics.

text: In

text: both

text: and

text: connectors

text: are

text: created

text: to

text: vector

text: slices

text: (e.g.

text: in

text: SVG

text: format)

text: even

text: for

text: raster

text: printers

text: ease

text: the

text: actual

text: reassembly.

text: Conversely,

text: instead

text: of

text: using

text: connectors,

text: This

text: is

text: because

text: slice

text: rescaling

text: is

text: usually

text: performed

text: internally

text: prior

text: glue,

text: or

text: screws,

text: in

text: the

text: subdivision

text: strives

text: to

text: create

text: a

text: part

text: to

text: projection,

text: and

text: scaling

text: a

text: raster

text: image

text: may

text: introduce

text: unnecessary

text: conﬁguration

text: that

text: allows

text: self-interlocking,

text: such

text: that

text: the

text: assembled

text: artifacts

text: such

text: as

text: image

text: blurring.

text: object

text: can

text: be

text: not

text: only

text: repeatedly

text: disassembled

text: and

text: reassembled,

text: but

text: also

text: strongly

text: connected

text: by

text: the

text: parts’

text: own

text: geometry.

text: Multiple

text: components

text: Batch

text: printing

text: Print,

text: pack

text: and

text: ship

text: paradigm

text: In

text: principle,

text: a

text: model

text: made

text: of

text: multiple

text: parts

text: can

text: be

text: printed

text: all

text: Delivering

text: a

text: printed

text: object

text: (e.g.

text: to

text: a

text: customer)

text: has

text: a

text: cost

text: that

text: at

text: once,

text: that

text: is,

text: pre-assembled.

text: However,

text: with

text: current

text: technologies

text: grows

text: as

text: the

text: size

text: of

text: the

text: pack

text: grows.

text: Thus,

text: it

text: is

text: important

text: to

text: in-

text: this

text: approach

text: might

text: still

text: have

text: several

text: drawbacks:

text: vestigate

text: how

text: a

text: model

text: can

text: be

text: split

text: into

text: easily

text: printable

text: parts

text: The

text: pre-assembled

text: model

text: can

text: be

text: too

text: large

text: to

text: ﬁt

text: the

text: printing

text: that

text: can

text: eventually

text: be

text: tightly

text: packed

text: in

text: a

text: box

text: and

text: reassembled

text: chamber,

text: whereas

text: each

text: single

text: constituting

text: part

text: would

text: ﬁt;

text: at

text: the

text: destination.

text: In

text: an

text: algorithm

text: that

text: performs

text: this

text: kind

text: The

text: model

text: is

text: small

text: enough,

text: but

text: it

text: contains

text: tangency

text: parts

text: which

text: of

text: split

text: is

text: proposed

text: based

text: on

text: a

text: user-controllable

text: tradeoff

text: between

text: c

text: ComputerGraphicsForum

text: c


text: M. Livesu, S. Ellero, J. Martínez, S. Lefebvre & M. Attene / From 3D Models to 3D Prints : an Overview of the Processing Pipeline [MKD99] MANI K., KULKARNI P., DUTTA D.: Region-based adaptive [PVRD03] PANDEY P., VENKATA REDDY N., DHANDE S. G.: Slic- slicing. Computer-Aided Design 31, 5 (1999), 317–333. 14 ing procedures in layered manufacturing: a review. Rapid prototyping journal 9, 5 (2003), 274–288. 2 [MN13] MICROSOFT, NETFABB: 3D model repair service. https: //modelrepair.azurewebsites.net/, 2013. 8 [PWLSH13] PRÉVOST R., WHITING E., LEFEBVRE S., SORKINE- [MRI00] MASOOD S., RATTANAWONG W., IOVENITTI P.: Part build HORNUNG O.: Make It Stand: Balancing shapes for 3D fabrication. orientations based on volumetric error in fused deposition modelling. 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MRS Bulletin 40, 11 (nov 2015), 943–950. 13, 20 [PC15] PAN Y., CHEN Y.: Smooth surface fabrication based on con- trolled meniscus and cure depth in microstereolithography. Journal of [RL16] REINER T., LEFEBVRE S.: Interactive modeling of support-free Micro and Nano-Manufacturing 3, 3 (2015), 031001. 16 shapes for fabrication. In Eurogrraphics 2016 - Short Papers (2016). 9, 20 [PCB01] PARK S.-M., CRAWFORD R. H., BEAMAN J. J.: Volumet- ric multi-texturing for functionally gradient material representation. In [RNDA13] ROLLAND-NEVIERE X., DOERR G., ALLIEZ P.: Robust Proceedings of the sixth ACM symposium on Solid modeling and appli- diameter-based thickness estimation of 3D objects. Graphical Models cations - SMA 01 (2001), Association for Computing Machinery (ACM). 75 (2013), 279–296. 7 13, 16 [RR86] ROSSIGNAC J. R., REQUICHA A. A.: Offsetting operations in [PDG99] PHAM D., DIMOV S., GAULT R.: Part orientation in stere- solid modelling. 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M., PANDE S.: Optimum part orientation in rapid single-camera sensing of printed physical user interfaces. In Proceed- prototyping using genetic algorithm. Journal of manufacturing systems ings of the 26th annual ACM symposium on User interface software and 31, 4 (2012), 395–402. 8, 10 technology (2013), ACM, pp. 447–456. 20 ∗ [PRD07] PANDEY P., REDDY N. V., DHANDE S.: Part deposition orien- [SDW 16] SONG P., DENG B., WANG Z., DONG Z., LI W., FU C.-W., tation studies in layered manufacturing. Journal of Materials Processing LIU L.: CoﬁFab: Coarse-to-ﬁne fabrication of large 3D objects. ACM Technology 185, 1 (2007), 125–131. 8 Transactions on Graphics 35, 4 (2016), 45:1–45:11. 5 (cid:13) c 2017TheAuthor(s) (cid:13) ComputerGraphicsForum c 2017TheEurographicsAssociationandJohnWiley&SonsLtd.

text: M.

text: Livesu,

text: S.

text: Ellero,

text: J.

text: Martínez,

text: S.

text: Lefebvre

text: M.

text: Attene

text: From

text: Models

text: to

text: Prints

text: an

text: Overview

text: of

text: the

text: Processing

text: Pipeline

text: MANI

text: K.,

text: KULKARNI

text: P.,

text: DUTTA

text: D.:

text: Region-based

text: adaptive

text: PANDEY

text: P.,

text: VENKATA

text: REDDY

text: N.,

text: DHANDE

text: S.

text: G.:

text: Slic-

text: slicing.

text: Computer-Aided

text: Design

text: ing

text: procedures

text: in

text: layered

text: manufacturing:

text: a

text: review.

text: Rapid

text: prototyping

text: journal

text: MICROSOFT,

text: NETFABB:

text: model

text: repair

text: service.

text: https:

font: NRORUA+NimbusMonL-Regu

text: //modelrepair.azurewebsites.net/,

text: PRÉVOST

text: R.,

text: WHITING

text: E.,

text: LEFEBVRE

text: S.,

text: SORKINE-

text: MASOOD

text: S.,

text: RATTANAWONG

text: W.,

text: IOVENITTI

text: P.:

text: Part

text: build

text: HORNUNG

text: O.:

text: Make

text: It

text: Stand:

text: Balancing

text: shapes

text: for

text: fabrication.

text: orientations

text: based

text: on

text: volumetric

text: error

text: in

text: fused

text: deposition

text: modelling.

text: ACM

text: Transactions

text: on

text: Graphics

text: The

text: International

text: Journal

text: of

text: Advanced

text: Manufacturing

text: Technology

text: PANETTA

text: J.,

text: ZHOU

text: Q.,

text: MALOMO

text: L.,

text: PIETRONI

text: N.,

text: CIGNONI

text: P.,

text: ZORIN

text: D.:

text: Elastic

text: textures

text: for

text: additive

text: fabrication.

text: ACM

text: MASOOD

text: S.,

text: RATTANAWONG

text: W.,

text: IOVENITTI

text: P.:

text: A

text: generic

text: Transactions

text: on

text: Graphics

text: algorithm

text: for

text: a

text: best

text: part

text: orientation

text: system

text: for

text: complex

text: parts

text: in

text: rapid

text: PAN

text: Y.,

text: ZHAO

text: X.,

text: ZHOU

text: C.,

text: CHEN

text: Y.:

text: Smooth

text: surface

text: fab-

text: prototyping.

text: Journal

text: of

text: Materials

text: Processing

text: Technology

text: rication

text: in

text: mask

text: projection

text: based

text: stereolithography.

text: Journal

text: of

text: Manu-

text: facturing

text: Processes

text: MCMAINS

text: S.,

text: SÉQUIN

text: C.:

text: A

text: coherent

text: sweep

text: plane

text: slicer

text: for

text: QU

text: X.,

text: STUCKER

text: B.:

text: A

text: surface

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text: method

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text: format

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text: manufacturing.

text: In

text: Proceedings

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text: ACM

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text: Journal

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text: QIU

text: Y.,

text: ZHOU

text: X.,

text: QIAN

text: X.:

text: Direct

text: slicing

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text: data

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text: MCMAINS

text: S.,

text: SMITH

text: J.,

text: WANG

text: J.,

text: SÉQUIN

text: C.:

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text: guaranteed

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text: rapid

text: prototyping.

text: The

text: International

text: Journal

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text: parts.

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text: ASME

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text: Advanced

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text: Technology

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text: Conference

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text: D.,

text: ZENG

text: L.,

text: YUEN

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text: Computer-Aided

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text: Applications

text: nAo.l:ogPireos-.

text: RICHARD

text: D.

text: T.,

text: CRAWFORD

text: R.

text: H.:

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text: orientation.

text: The

text: University

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text: at

text: Austin,

text: CIRP

text: Annals-Manufacturing

text: Technology

text: OXMAN

text: N.:

text: Variable

text: property

text: rapid

text: prototyping.

text: Virtual

text: and

text: REEVES

text: P.

text: E.,

text: COBB

text: R.

text: C.:

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text: stereolithography

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text: techniques.

text: Rapid

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text: Physical

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text: PARK

text: S.

text: C.:

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text: objects

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text: uniform

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text: thickness.

text: Computer-Aided

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text: [RCM

text: REINER

text: T.,

text: CARR

text: N.,

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text: BACHER

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text: PRÉVOST

text: R.,

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text: Proceed-

text: ings

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text: Vision,

text: Modeling

text: and

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text: Workshop

text: (VMV)

text: RANEY

text: J.

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text: LEWIS

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text: mesoscale

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text: microstereolithography.

text: Journal

text: of

text: REINER

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text: LEFEBVRE

text: S.:

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text: Micro

text: and

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text: fabrication.

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text: Papers

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text: Association

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text: Computing

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text: J.

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text: International

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text: SITTHI-AMORN

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text: ADZHIEV

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text: using

text: additive

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text: techniques.

text: Procedia

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text: The

text: International

text: Journal

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text: Advanced

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text: KISHAWY

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text: Global

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text: R.,

text: BROSCH

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text: Patent

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text: KOBBELT

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text: High-resolution

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text: SCHUMACHER

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text: J.,

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text: Polyjet

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text: algorithm.

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