Title: Event-Driven Dynamic Scene Depth Completion
Venue: NeurIPS 2025
Accepted: yes
Keywords: depth completion

event guidance

dynamic scene
Abstract: Depth completion in dynamic scenes poses significant challenges due to rapid ego-motion and object motion, which can severely degrade the quality of input modalities such as RGB images and LiDAR measurements. Conventional RGB-D sensors often struggle to align precisely and capture reliable depth under such conditions. In contrast, event cameras with their high temporal resolution and
sensitivity to motion at the pixel level provide complementary cues that are beneficial in dynamic environments. To this end, we propose EventDC, the first event-driven depth completion framework. It consists of two key components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF). Both modules adaptively learn the two fundamental components of convolution operations: offsets and weights conditioned on motion-sensitive event streams. In the encoder, EMA leverages events to modulate the sampling positions of RGB-D features to achieve pixel redistribution for improved alignment and fusion. In the decoder, LDF refines depth estimations around moving objects by learning motion-aware masks from events. Additionally, EventDC incorporates two loss terms to further benefit global alignment and enhance local depth recovery. Moreover, we establish the first benchmark for event-based depth completion comprising one real-world and two synthetic datasets to facilitate future research. Extensive experiments on this benchmark demonstrate the superiority of our EventDC. [Project page](https://yanzq95.github.io/projectpage/EventDC/index.html).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes EventDC, the first event-driven depth completion framework tailored for dynamic scenes. It addresses the common challenges of RGB-LiDAR misalignment and depth degradation caused by fast ego-motion and object motion, which conventional sensors and fusion approaches struggle to handle. The method introduces two novel modules:\n\n1. Event-Modulated Alignment (EMA): leverages event features to predict spatial offsets for RGB and LiDAR features, improving global alignment and multi-modal fusion during encoding.\n\n2. Local Depth Filtering (LDF): focuses on decoder-side refinement near moving objects, using event-informed motion masks and adaptive depth filtering.\n\nIn addition, the paper presents the first benchmark for event-based depth completion, including real-world (EventDC-Real), semi-synthetic (EventDC-SemiSyn), and synthetic (EventDC-FullSyn) datasets. Extensive experiments show consistent SOTA performance across all settings, with detailed ablation studies and complexity analysis.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. Interesting problem and new benchmark datasets: This paper addresses a real yet underexplored scenario, i.e., depth completion in highly dynamic scenes, where existing RGB-D systems underperform. Furthermore, It introduces a three-part benchmark (real/semi-synthetic/full synthetic), which is a notable contribution to the field, likely to promote further research in event-based depth learning.\n\n2. Well-motivated design: The EMA and LDF modules introduce event-driven dynamic convolution for both global alignment and local refinement, exploiting events’ high temporal resolution effectively.\n\n3. Strong performance: EventDC outperforms 9+ strong baselines on three datasets with consistent improvements in many metrics without post-processing or additional priors.\n\n4. Comprehensive experiments: Detailed studies isolate the contribution of each modality (RGB, event, sparse depth), component (EMA/LDF), and design choices (dynamic convolution), which strengthens the claims. Comprehensive experiments show the superiority of the proposed method. \n\nMinor Weaknesses:\n\t\nWhile the paper is overall strong and well-executed, a few aspects could benefit from additional clarification or discussion:\n\n1. Dependence on high-quality events: As acknowledged, the method assumes reliable event streams and well-synchronized RGB-D-event input. A brief discussion on robustness to sensor noise or misalignment would improve practical insights.\n\n2. Benchmark diversity: While the EventDC benchmark is a valuable contribution, especially with a real-world component, its coverage of diverse environmental conditions (e.g., outdoor lighting, long-range scenes) could be discussed to help readers understand the scope and future extension possibilities.\n\nNone of these points undermine the core contributions, and they can be addressed through minor clarifications.\n\nQUESTIONS:\n1. Use of the benchmark: Do you plan to release the simulation or preprocessing tools used to create EventDC-SemiSyn and EventDC-FullSyn? This could encourage broader community engagement.\n\n2. Failure cases: If possible, could you describe typical scenarios where EventDC might underperform (e.g., repetitive textures or extremely low motion regions)? Even a qualitative description would enhance the paper’s completeness.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-27"}

comments: SUMMARY:
The paper introduces a new event-driven depth completion framework, named EventDC, which is designed to address the challenges of dynamic environments. The model mainly consists of two modules, Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF), to mitigate the misalignment caused by ego-motion and local depth inaccuracies due to object motion. Two dedicated loss constraints are used for supporting training. Besieds, it builds an event-based benchmark for depth completion.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The paper is well-written and formatted.
2. In this paper's setting, the results seems outperform other method of depth completion.
3. The EventDC benchmark is a good baseline for future researchs.
Weakness:
1. In real world, if there are rich and complicated motions in the environment, not only be the color image not reliable, the sparse depth is not reliable neither. In this situation, how to and why use event streams to fix or complete the unreliable depth map? 
2. The model architecture lacks novelty. In the EventDC, the EMA and LDF all consists of basic DCN blocks, which is a commonly-used module.
3. Another key concern is the fairness of the comparison experiments. While in the motion-rich environments, the image data are designed or set to possessing blur artifacts. It may not fair for image-based methods. Are there futher experiments for demonstration?

QUESTIONS:
As listed in the "Weaknesses". I would consider raise the score if the rebuttal is satisfying.

LIMITATIONS:
None.
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an EventDC model that leverages RGB, LiDAR, and event images to generate dense depth maps. Two key components—event-modulated alignment and local depth filtering—are designed based on deformable convolution. Meanwhile, three datasets are constructed, including a real-world dataset, a semi-synthetic dataset, and a fully synthetic dataset. Experiments on these datasets demonstrate the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This work is likely the first to utilize event images specifically for depth completion with dynamic environments.\n2. The proposed method is both reasonable and effective.\n3. The paper is well-written and easy to follow.\n4. Additionally, relevant datasets are provided.\n\nWeaknesses:\n1. Figure 2’s layout is confusing, affecting clarity of the network structure.\n2. Offset notations in equations (1), (3a), and (3b) are inconsistent.\n3. Lack of explanation for different deformable convolution weight designs in encoder and decoder.\n4. Improvements seem more on static backgrounds than on dynamic objects, which contradicts the main goal.\n5. Missing evaluation of model performance without the event stream input.\n\nQUESTIONS:\n1. In Figure 2, should the Event stream E be positioned in the middle while the sparse depth S is at the bottom?\n\n2. It is suggested to unify the notation of the offset in equations (1), (3a), and (3b).\n\n3. Why are the weight designs in the deformable convolution different between the encoder and decoder? What would happen if they used the same design?\n\n4. It is expected that EventDC improves depth estimation around moving objects, but in Figure 4, it appears that improvements are more pronounced in the distant background, which might be less dynamic. Could you explain why? It would be helpful to see more results on dynamic objects.\n\n5. How does the model perform without the event stream but with the designed deformable convolutions?\n\nLIMITATIONS:\nThe rationale behind different deformable convolution designs in encoder and decoder is unclear. \n\nThe results mainly show improvements on static backgrounds rather than dynamic objects, which contradicts the main goal. \n\nThere is no ablation study to isolate the contribution of the event stream.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel event-driven dynamic scene depth completion pipeline. The core research question is how to effectively predict dense depth from event camera-based sparse measurements in highly dynamic environments. This task is particularly difficult because object movement can create severe artifacts on RGB cameras and LiDAR measurements. To address these issues, this work introduces EventDC, which leverages the high temporal resolution and motion sensitivity of event cameras.  Its key design incorporates two main components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF). Crucially, the paper also establishes the first event-based depth completion benchmark, comprising real-world, semi-synthetic, and fully synthetic datasets. The evaluation shows that EventDC achieves competitive efficiency and accuracy.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The proposed pipeline addresses depth completion in the dynamic environment. This is very important for real-world applications such as autonomous vehicles and augmented reality.\n- The source code and datasets are promised to be released.\n\nWeaknesses:\n- My main concern is on the latency of the pipeline. The proposed work targets a highly dynamic environment, but the proposed pipeline takes >40ms to run based on Table 5.\n\nQUESTIONS:\n- Is it possible to adjust the network architecture to trade-off between accuracy and latency to fit different deployment environments?\n\nLIMITATIONS:\nThe paper acknowledges that while EventDC achieves promising results, its Event-Modulated Alignment and Local Depth Filtering modules introduce additional computational costs. This could potentially limit deployment on resource-constrained devices. While EventDC maintains competitive efficiency, its inference time is 41.5 ms. This computational latency directly determines the maximum frequency at which the system can generate depth inferences in a real-time setting. However, the paper does not explicitly discuss the implications of network latency on the overall system's real-time performance, nor does it detail how such latency might affect the amount of event information available for processing during different inference cycles.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}