---
license: other
license_name: cc-nc-sa
license_link: https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
task_categories:
- object-detection
- depth-estimation
- image-segmentation
tags:
- warehouse
- synthetic
- isaac-sim
- walkthrough
- stereo
- 3d-bounding-boxes
size_categories:
- n>100K
# The automatic dataset viewer cannot interpret per-frame video; left on it
# latches onto the timestamp CSVs and shows columns that misrepresent the
# dataset. The Space linked below is the real previewer.
viewer: false
---

# Staer Warehouses — v0.5
Synthetic stereo walkthroughs through procedurally-generated and curated
warehouses, rendered with NVIDIA Isaac Sim. Each scene includes RGB,
depth, semantic segmentation, instance segmentation, IMU, and a
scene-graph JSON with per-asset class labels and oriented bounding boxes
(OBBs) in world coordinates.

👉 **Interactive preview:** https://huggingface.co/spaces/staerrobotics/warehouses-viewer

![overview](./overview.png)

## What's new in this release
- **Full per-pixel ground truth now ships with every walkthrough** — 16-bit inverse depth, semantic segmentation, and instance segmentation as lossless FFV1 videos (`depth0/`, `semantic0/`, `instance0/`), plus IMU (`imu0/data.csv`). Previous releases included only the RGB stream and a lossy preview grid; the depth shown there was a visualisation, not data.
- **Stereo** — both cameras of the ZED-X-like rig (`cam0`/`cam1`) are now included per walkthrough.
- **Structured scene descriptions** — every scene ships with a `scene_graph.json` that enumerates each object in the warehouse with its class label, oriented bounding box (OBB) in world coordinates, axis-aligned bounding box, and pose (position + quaternion). One file per scene, shared across all walkthroughs of that scene.
- **All authored trajectories shipped per scene** — all 147 authored trajectories across 15 scenes, giving substantially more pose / viewpoint diversity for the same set of warehouse environments.

## Contents
- 15 scenes, 147 walkthrough trajectories total
- Per walkthrough: stereo cam0/cam1, ~300–2400 frames at 20 fps (≈15s–2min camera paths)
- 1920×1080 H.265 RGB per camera; 16-bit inverse depth, semantic and instance ID maps as lossless FFV1 (`gray16le`) videos
- Per-scene `scene_graph.json` with OBB + class for every object
- IMU + ground-truth poses per camera, EuRoC-ASL convention

## Scenes
| # | Name | Walkthroughs | Objects | Top classes |
|---|------|--------------|---------|-------------|
| 01 | scene01 | 9 | 33254 | box(14128), crate(12614), pallet(2728) |
| 02 | scene02 | 11 | 11599 | box(3742), crate(2421), pallet(1512) |
| 03 | scene03 | 10 | 14661 | rack(4150), box(2430), pallet(2010) |
| 04 | scene04 | 9 | 18339 | box(5067), rack(3240), crate(2713) |
| 05 | scene05 | 9 | 15653 | box(4583), rack(3300), crate(2595) |
| 06 | scene06 | 12 | 4303 | box(587), ceiling_light(448), rack(440) |
| 07 | scene07 | 8 | 15294 | box(5341), crate(2850), pallet(2305) |
| 08 | scene08 | 8 | 5085 | box(1655), crate(1041), pallet(534) |
| 09 | scene09 | 10 | 360 | drum(150), jerrican(52), railing(36) |
| 10 | scene10 | 10 | 945 | conveyor(417), belt_item(144), box(142) |
| 11 | scene11 | 10 | 471 | box(206), pallet(66), crate(36) |
| 12 | scene12 | 11 | 441 | box(177), belt_item(43), tote(36) |
| 13 | scene13 | 10 | 677 | box(189), shelving(153), bin(107) |
| 14 | scene14 | 10 | 643 | box(246), pallet(60), bin(54) |
| 15 | scene15 | 10 | 567 | box(145), tote(66), drum(63) |

## Class taxonomy
51 unique classes across the release. Top 20 by count:

| Class | Count |
|---|---|
| `box` | 38,638 |
| `crate` | 26,046 |
| `pallet` | 14,809 |
| `rack` | 13,769 |
| `ceiling_light` | 4,620 |
| `pipe` | 3,588 |
| `wall` | 2,964 |
| `fire_equipment` | 2,312 |
| `wiring` | 2,236 |
| `ceiling` | 1,576 |
| `truss` | 1,320 |
| `floor` | 1,320 |
| `decor` | 1,172 |
| `camera` | 692 |
| `ventilation` | 660 |
| `phone` | 660 |
| `column` | 660 |
| `wall_light` | 576 |
| `electrical_panel` | 576 |
| `alarm` | 552 |

## File layout
```
warehouse-v0.5/
├── README.md
├── overview.png              # 5×3 montage of scene thumbnails
├── scene01/
│   ├── thumbnail.jpg         # representative frame for the scene
│   ├── scene_graph.json      # objects[]: class, OBB, pose (per-scene)
│   └── walkthroughs/
│       ├── 001/
│       │   ├── preview.mp4   # 2×2 grid: RGB | depth | semantic | instance
│       │   ├── manifest.json # EuRoC-ASL header (timestamps, fps, frameCount)
│       │   ├── metadata.json # asset metadata (modalities, resolution, …)
│       │   ├── cam0/
│       │   │   ├── poses.csv   # T_WC (Hamilton w,x,y,z) at frame timestamps
│       │   │   ├── sensor.yaml # intrinsics, T_BS
│       │   │   ├── data.csv    # frame index: timestamp per video frame
│       │   │   └── video.mp4   # RGB (H.265)
│       │   ├── cam1/  …         # stereo right camera, same files
│       │   ├── depth0/
│       │   │   ├── video.mkv         # 16-bit inverse depth (FFV1 gray16le)
│       │   │   ├── depth_ranges.json # per-frame inv_min/inv_max (see below)
│       │   │   └── data.csv
│       │   ├── semantic0/
│       │   │   ├── video.mkv         # 16-bit class IDs (FFV1 gray16le)
│       │   │   ├── id_to_labels.json # class ID → label name
│       │   │   └── data.csv
│       │   ├── instance0/
│       │   │   ├── video.mkv         # 16-bit instance IDs (FFV1 gray16le)
│       │   │   ├── id_to_labels.json # instance ID → prim path
│       │   │   └── data.csv
│       │   └── imu0/
│       │       └── data.csv    # gyro + accel at 400 Hz, EuRoC columns
│       ├── 002/  …
│       └── NNN/  …
└── scene02/ … scene15/
```
scene_graph.json is shared across all walkthroughs of a scene (same underlying USD); cam poses differ per walkthrough. Depth and segmentation are rendered from cam0 (the left camera). Row *i* of each `data.csv` gives the timestamp of video frame *i*.

## Decoding the 16-bit streams
`video.mkv` files are lossless FFV1, one 16-bit grayscale plane per frame — every pixel value is exactly the value the renderer wrote. Extract frames with ffmpeg or read them directly with PyAV:

```bash
ffmpeg -i depth0/video.mkv -f image2 -pix_fmt gray16be depth_%06d.png
```

```python
import av, numpy as np
frames = [f.to_ndarray() for f in av.open('depth0/video.mkv').decode(video=0)]
```

**Depth** is stored as per-frame-normalised inverse depth. To recover metric depth for frame `i` with 16-bit value `v` (0 = invalid / no hit):

```python
r = depth_ranges[i]  # depth_ranges.json, ordered like the video frames
inv = r['inv_min'] + (v - 1) / 65534.0 * (r['inv_max'] - r['inv_min'])
depth_m = 1.0 / inv  # metres
```

**Semantic / instance** pixel values are integer IDs; map them to labels via the adjacent `id_to_labels.json`.

## Coordinate conventions
- Right-handed world frame, +Z up.
- Camera convention: T_WC = T_WB · T_BS where T_BS comes from `sensor.yaml`.
- Quaternions are Hamilton, `(w, x, y, z)`, with positive-w convention.
- Distances in metres; timestamps in nanoseconds (EuRoC-ASL).

## Rolling releases
This dataset is on a rolling-release cadence: minor versions ship regularly with quality improvements and bug fixes layered onto the previous release rather than waiting for a single large drop. The scene set and modalities are stable across minor versions; what changes is per-scene asset quality, trajectory authoring, and annotation coverage.

Known issues we are actively addressing in subsequent minor releases:
- **Trajectory coverage** — a small number of authored walkthroughs are still missing from individual scenes and will be backfilled in the next minor release.
- **Asset material quality** — a subset of NVIDIA SimReady props ship with incomplete MDL/texture chains that fall back to default (white or near-black) shaders; we are auditing and patching these per scene.
- **Lighting consistency** — the curated NVIDIA themed shells (scenes 09–15) have noticeably dimmer baked lighting than the procedural scenes (01–08); we are normalising exposure across the set.
- **Trajectory smoothness** — a few canvas-authored trajectories include sharp orientation changes that can confuse motion estimators; we are re-authoring with stricter smoothness constraints.

Subscribe to the dataset on Hugging Face to be notified when a new minor version is published. Each release ships with its own version tag and a short changelog summarising the deltas from the previous release.

## Citation
If you use this dataset, please cite Staer Robotics.

```bibtex
@dataset{staer_warehouses_v0.5, title={Staer Warehouses (v0.5)}, year={2026}, publisher={Staer Robotics}, url={https://huggingface.co/datasets/staerrobotics/warehouses}}}
```