PAPER: IntroductionDepth estimation is a fundamental task in computer vision, with key applications in areas such as robotics, autonomous driving, and augmented reality. Traditional stereo vision techniques rely on synchronized cameras to capture images and infer depth by finding correspondences between them. However, these methods often struggle in low-light and fast-motion conditions. Moreover, conventional cameras produce large amounts of redundant data by capturing entire images at fixed intervals, leading to inefficiencies in both data storage and processing.Unlike conventional cameras, event cameras operate asynchronously, detecting per-pixel brightness changes (called "events") [1][2][3]. This provides high temporal resolution and robustness to motion blur, making them well suited for dynamic scenes. Their sparse output enables efficient processing of only relevant areas, making them ideal for real-time tasks such as visual odometry (VO) / SLAM.Harnessing deep learning for depth estimation with event cameras has the potential to transform such applications. However, adapting neural networks to event data remains challenging due to its asynchronous stream-like nature. Moreover, the scarcity of event camera datasets with ground truth depth [4,5] results in limited training data, which can lead to overfitting [6]. While simulating data is one way to address this issue, generalizing models trained on simulation to real-world scenarios is far from trivial due to differences in data distribution [7][8][9][10]. Thus, instead of directly processing events (which is prone to overfitting and extensive training time) we rely on intermediate event representations informed by the scene geometry.One promising approach for 3D reconstruction is back-projecting events as rays into space and capturing their intersection densities as disparity space images (DSIs) [11]. DSIs from two or more cameras can be fused, eliminating the need for event synchronization between cameras. This reduces complexity and allows for more robust depth estimation. The Multi-Camera Event-based Multi-View Stereo (MC-EMVS) method [12] recently produced state-of-the-art (SOTA) results, outperforming other techniques in depth benchmarks across several metrics. To obtain pixel-wise depth estimates from a DSI, MC-EMVS selects the disparity level with the highest ray count, effectively using an argmax operation. Ray counting is used as a proxy for finding 3D edges where the rays intersect. A selective threshold filter is applied to predict depth only for pixels with sufficient ray counts.While straightforward, this approach does not fully utilize the potential of DSIs. It is susceptible to noise and cannot effectively extract cues from surrounding pixels and more complex patterns across disparity levels. Consequently, it leads to fewer pixels obtaining depth estimation and less accurate depth predictions than the DSI might potentially allow for. Therefore, we need more effective approaches for extracting depth from DSIs, that are more reliable, accurate, and produce more depth estimates, by adequately recognizing complex ray intersection patterns.Our Contribution. We propose a novel deep learning framework for event-based depth estimation that is optimized for SLAM scenarios and addresses the aforementioned limitations. An overview is illustrated in Fig. 1. Our approach estimates pixel-wise depth from a DSI using a neural network with 3D convolutions and a recurrent structure. The framework is directly applicable to both monocular and stereo settings. Our key contributions include:• Learning-based Local Processing: For each selected pixel, a small local subregion of the DSI (Sub-DSI) is used as input to the neural network. This novel design leverages the inherent sparsity of event data and DSIs to efficiently process and produce only relevant information for sparse depth-related tasks. (Sec. 3). • Enhanced Data Utilization: Our model captures complex patterns within the DSI, increasing depth prediction accuracy and the number of pixels for which depth can be reliably estimated.Limiting the input to small local subregions around selected pixels enhances generalization by preventing the network from overfitting to dynamics specific to the training scenes and augmenting the available training set, as each Sub-DSI serves as an individual data instance. • Efficiency and Scalability: By adopting our Sub-DSI approach, we obtain small independent inputs of fixed size. This enables full parallelization and provides an ultra-light network that has the ability to handle any camera resolution with constant very short inference time. Our network architecture allows the processing of DSIs of variable depth resolution. (Sec. 3.2). • Comprehensive Experiments: We evaluate our model on both monocular and stereo data from the standard datasets MVSEC [4] and DSEC [13] using cross-validation. It outperforms the state of the art by a large margin on ten figures of merit. Even using monocular data, our model achieves performance comparable to SOTA methods that require stereo data (Sec. 4). We show downstream applicability and robustness to imperfect (noisy) camera poses.To the best of our knowledge, our work is the first learning-based multi-view stereo method to (i) use camera poses along with events as input, which is crucial for accurate depth estimation over long intervals (as used in SLAM [14,15]); (ii) demonstrate successful and robust depth prediction on real-world event data from DSIs; (iii) report good generalization on all three MVSEC indoor flying sequences [5], even when compared to multi-modal methods that combine stereo intensity frames with events. We provide code, trained models and video results for clarity and reproducibility.
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REVIEW
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**Summary Of The Paper**

The paper introduces **DERD-Net**, a novel deep learning framework for event-based depth estimation that leverages **Disparity Space Images (DSIs)** constructed from event camera data. Unlike prior methods that rely on explicit event synchronization or simplistic heuristics (e.g., argmax over ray counts), DERD-Net employs a **learning-based local processing strategy** that extracts depth from small sub-regions of DSIs (Sub-DSIs) using a combination of **3D convolutions** and **Gated Recurrent Units (GRUs)**. The framework is evaluated on the **MVSEC** and **DSEC** datasets, showcasing competitive performance compared to state-of-the-art methods such as **MC-EMVS** and **SGM**, particularly in monocular and stereo settings. Key innovations include:

- **Local Sub-DSI Processing**: Efficient depth estimation using small, localized regions of DSIs, reducing overfitting and enhancing generalization.
- **Improved Data Utilization**: Better exploitation of complex ray intersection patterns in DSIs, leading to enhanced depth prediction accuracy and coverage.
- **Lightweight and Scalable Architecture**: Fixed-sized Sub-DSI inputs allow for parallel processing and ultra-low inference times, irrespective of camera resolution.
- **Robustness to Real-World Conditions**: Demonstrated resilience to noisy camera poses and varying environmental conditions.

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**Strengths**

1. **Novelty in DSI Processing**: DERD-Net introduces a learning-based method to exploit DSIs, which are rarely used in event-based depth estimation. This is a notable departure from earlier heuristic approaches such as argmax or thresholding.

2. **Comprehensive Evaluation**: The paper conducts extensive experiments on both **monocular and stereo** data from **MVSEC** and **DSEC** datasets. The results are compared against multiple state-of-the-art methods, including **MC-EMVS**, **SGM**, and **EMVS**, offering a solid comparative basis.

3. **Architectural Innovation**: The use of **3D convolutions** and **GRUs** is well-explained and justified for modeling the depth dimension and sequential dependencies in DSIs. The lightweight architecture supports real-time inference, aligning with the goal of event-based vision systems.

4. **Empirical Evidence of Robustness**: The paper provides qualitative and quantitative demonstrations of the model's performance under **noisy camera poses** and different **filter thresholds** (F_orig vs. F_denser), suggesting robustness to variations in input quality and data selection criteria.

5. **Practical Applicability**: Emphasis is placed on the suitability of DERD-Net for **SLAM** and **real-time applications**, addressing a key bottleneck in event-based vision systems.

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**Weaknesses**

1. **Lack of Statistical Validity (Major Concern)**: The paper does not report **error bars**, **confidence intervals**, or **statistical significance tests** for the reported metrics. This makes it impossible to assess the **reliability** of the performance gains over baselines. For example, the reported reductions in **MAE** and **bad-pix** are not backed by statistical validation, undermining the strength of the conclusions.

2. **Incomplete Comparison with Relevant Baselines (Serious Issue)**: Several prominent methods, such as **DDES** and **EIT-Net**, are referenced in the related work but are **not included** in the experimental evaluation. This omission weakens the novelty claim and raises questions about the comprehensiveness of the comparison.

3. **Ambiguous Novelty Claims**: The paper asserts that DERD-Net is the **“first learning-based multi-view stereo method”** to incorporate **camera poses** with events. However, **MC-EMVS** also uses camera poses, albeit in a non-learning fashion. The distinction between the two is unclear, and the paper does not convincingly argue for the novelty of this aspect.

4. **Insufficient Explanation of Sub-DSI Trade-offs**: While the paper explores the impact of **sub-DSI size** on performance, it does not systematically quantify the **trade-off between accuracy, computational cost, and parameter count**. This limits the understanding of how the method scales and whether smaller sub-DSI sizes could offer similar benefits with fewer resources.

5. **Scale-Invariance of Metrics**: All evaluation metrics (**MAE**, **MedAE**, **bad-pix**, **δ-accuracy**) are **scale-sensitive**, which is problematic when comparing across different depth ranges (e.g., **MVSEC** vs. **DSEC**). The lack of **scale-invariant metrics** (e.g., **SILog**) undermines the comparability of results across different datasets.

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**Questions For The Authors**

1. Why are **error bars**, **confidence intervals**, or **statistical significance tests** not reported for the performance metrics? How confident can we be in the significance of the reported improvements?

2. Why are **DDES** and **EIT-Net** not included in the experimental comparisons, despite being cited in the related work? How would their inclusion affect the conclusion?

3. How does DERD-Net differ from **MC-EMVS** in its use of **camera poses**? Is the integration of camera poses truly novel, or is it just a refinement of existing techniques?

4. What systematic analysis was done to evaluate the **trade-off between sub-DSI size, accuracy, and computational cost**? Could a smaller sub-DSI size (e.g., 5×5) achieve similar performance with reduced computational burden?

5. Why are **scale-invariant metrics** (e.g., **SILog**) not used in the evaluation? How does this affect the interpretation of results across different depth ranges (e.g., **MVSEC** vs. **DSEC**)?

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**Limitations Not Addressed By The Authors**

- **Generalizability Beyond MVSEC/DSEC**: The experiments are restricted to **MVSEC** and **DSEC** datasets. There is no evidence of performance on other event-based datasets or under different environmental conditions (e.g., extreme lighting, occlusions).

- **Failure Modes and Edge Cases**: The paper does not discuss **failure cases** or **limitations** of the method under adverse conditions (e.g., low-event-rate scenes, extreme motion, or poor visibility).

- **Scalability to Large Scenes**: The paper does not evaluate the performance of DERD-Net on **large-scale scenes** or with **high-resolution event cameras**, which could expose limitations in the method’s scalability.

- **Ethical and Societal Implications**: The paper does not address potential **ethical or societal impacts** of the method, such as **privacy concerns** or **misuse in surveillance systems**, despite the growing importance of ethical AI practices.

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**Soundness**: 3 (Good)

- The paper presents a well-defined method and provides reasonable experimental support. However, the lack of statistical rigor and incomplete comparisons weaken the soundness of the claims.

**Contribution**: 3 (Good)

- The method offers a novel approach to processing DSIs with a learning-based framework, contributing to the emerging area of event-based depth estimation. However, the novelty is somewhat diluted by the exclusion of relevant baselines and insufficient differentiation from prior work.

**Confidence**: 3 (Moderate)

- The method is plausible and well-described, but the lack of statistical validation and incomplete comparisons reduce confidence in the robustness of the findings.

**Rating**: 6 (Accept with Major Revision)

**Brief Justification For Rating**:

The paper presents a novel and technically sound approach to event-based depth estimation using DSIs. Its focus on local processing and lightweight architecture is commendable and aligns with the goals of real-time, event-driven vision systems. However, the lack of statistical validation, incomplete comparisons with relevant baselines, and insufficient discussion of limitations severely undermine the credibility of the claims. Significant revisions are required to address these issues before the paper can be accepted for publication.

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