Title: DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method
Venue: NeurIPS 2025
Accepted: yes
Keywords: Scene flow estimation

Point clouds

Efficient and scalable vision
Abstract: Previous dominant methods for scene flow estimation focus mainly on input from two consecutive frames, neglecting valuable information in the temporal domain. While recent trends shift towards multi-frame reasoning, they suffer from rapidly escalating computational costs as the number of frames grows. To leverage temporal information more efficiently, we propose DeltaFlow ($\Delta$Flow), a lightweight 3D framework that captures motion cues via a $\Delta$ scheme, extracting temporal features with minimal computational cost, regardless of the number of frames. Additionally, scene flow estimation faces challenges such as imbalanced object class distributions and motion inconsistency. To tackle these issues, we introduce a Category-Balanced Loss to enhance learning across underrepresented classes and an Instance Consistency Loss to enforce coherent object motion, improving flow accuracy. Extensive evaluations on the Argoverse 2, Waymo and nuScenes datasets show that $\Delta$Flow achieves state-of-the-art performance with up to 22\% lower error and $2\times$ faster inference compared to the next-best multi-frame supervised method, while also demonstrating a strong cross-domain generalization ability. The code is open-sourced at https://github.com/Kin-Zhang/DeltaFlow along with trained model weights.


Peer reviews:
comments: SUMMARY:
This paper introduces DeltaFlow, a novel and efficient motion representation. DeltaFlow models short-term motion as a delta between adjacent feature flows, which can be integrated into a recurrent encoder-predictor-decoder framework. The authors claim that DeltaFlow provides better motion cues while reducing computational cost and memory footprint. Experiments on standard benchmarks including Moving MNIST, KTH, Human3.6M, and KITTI demonstrate that DeltaFlow achieves competitive or improved prediction performance compared to existing motion representations such as optical flow and f-AR, with fewer parameters and faster inference.

STRENGTHS AND WEAKNESSES:
$\textbf{Strengths}$

1. DeltaFlow encodes motion as the difference between successive feature flows, which leads to a more compact representation compared to optical flow and f-AR, with reduced model size and faster runtime.

2. The proposed method is evaluated on a wide range of datasets and shows consistent improvements in prediction accuracy and efficiency.

3. The integration of DeltaFlow into a multi-stage recurrent architecture demonstrates good scalability and practical utility with fewer parameters and lower memory usage.

$\textbf{Weaknesses}$

1. The paper should provide more detailed technical explanations on how the decay factor $\lambda$ is set, as the relative importance of preceding and succeeding frames may vary across different scenarios.

2. The experimental section does not include comparisons with recent state-of-the-art models such as Floxels, which limits the completeness of the evaluation.

QUESTIONS:
please refer to weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-28

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ∆Flow, a lightweight 3D framework for efficient multi-frame scene flow estimation.  The key component of ∆Flow is the Δ scheme that captures temporal motion cues by computing differences between voxelized frames, maintaining computational efficiency regardless of the number of frames. Additionally, ∆Flow introduces a Category-Balanced Loss to handle class imbalance and an Instance Consistency Loss to enforce coherent motion for individual objects. Experiments show that ∆Flow achieves state-of-the-art performance with lower error and faster inference speed, and strong cross-domain generalization across the existing multi-frame supervised methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- This paper proposes the Δ scheme, which maintains computational efficiency regardless of the number of frames compared to previous multi-frame methods.\n- This paper introduces the Category-Balanced Loss and Instance Consistency Loss, which tackle the critical imbalanced object class distributions and motion inconsistency within objects in scene flow estimation. Ablation study also demonstrates the effectiveness of these two loss functions.\n- The paper is clearly written, and the experiments are adequate.\n\nWeaknesses\n- The main limitation acknowledged by the authors lies in ΔFlow's dependence on ground-truth annotations for supervised training, which would cause a domain gap in real-world settings, while obtaining accurate scene flow annotations is both challenging and expensive.\n- Although Δflow can efficiently process multiple frames, the maximum total number of frames for a single input is still only 15 frames, which is too low for real videos with high frame rates.\n\nQUESTIONS:\n- Can the authors provide more real-world arbitrary video inference results to better demonstrate the performance?\n- In Table 3,  the authors argued that \"long-ago frames become less informative for predicting current motion\". Is this related to the specific data distribution? When the average motion between frames is different, the amount of information contained in certain time intervals will be different. It seems that the amount of data used for training and evaluation is still relatively limited.\n\nLIMITATIONS:\nPlease refer to Weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n∆Flow introduces an innovative and efficient multi-frame scene flow estimation method that leverages a differential framework to address challenges in autonomous driving scenarios. By incorporating a ∆ scheme, Category-Balanced Loss, and Instance Consistency Loss, the method enhances dynamic object estimation and improves efficiency, achieving approximately 2× faster inference compared to existing methods, while maintaining high accuracy. Experimental results on the Argoverse 2 and Waymo datasets demonstrate superior performance, particularly in dynamic object estimation. However, further comparisons with a broader range of methods, detailed analysis of computational complexity, and validation on more diverse datasets such as KITTI or nuScenes would strengthen the paper’s claims and provide a more comprehensive evaluation of its generalization capabilities and practical applicability.\n\nSTRENGTHS AND WEAKNESSES:\nStrength: Clear Methodology: The paper presents a solid and well-explained methodology for efficient multi-frame scene flow estimation. The approach employs a novel ∆ scheme for temporal information processing, which allows the method to scale effectively without expanding feature size. This is a key advantage, especially in autonomous driving applications that often require real-time, large-scale data processing.\n\nThorough Experiments: The authors provide comprehensive experimental results across two widely recognized autonomous driving datasets—Argoverse 2 and Waymo. The experiments demonstrate that ∆Flow achieves state-of-the-art performance in terms of both accuracy and efficiency. This solid empirical validation enhances the credibility of the proposed method.\nClarity:Well-Structured Presentation: The paper is well-organized and easy to follow. Key concepts like the ∆ scheme, network architecture, and loss functions are explained in detail, with helpful visualizations (e.g., diagrams and charts) to support the narrative.\n\nWeaknesses:\nSensitivity to Noise:The differential method proposed in the paper improves computational efficiency, but in real-world environments, especially under the influence of sensor noise, occlusion, and other factors, the differential method may be affected by noise. This could lead to errors in dynamic object scene flow estimation, especially with low-quality or noisy input data. Therefore, the model's robustness in noisy environments needs further improvement and validation.\n\nLack of Computational Resource and Real-time Performance Analysis:Although the paper mentions that ∆Flow is faster in inference speed, it lacks a detailed quantitative analysis of computational complexity, memory usage, and computational resource requirements. For real-world deployment in autonomous driving systems, especially on resource-constrained devices, the model's real-time performance and resource consumption will directly impact its feasibility and application. A more detailed performance evaluation is needed in future work.\n\nDependence on Labeled Data:Currently, ∆Flow relies on supervised learning with labeled data for training, meaning it requires a large amount of annotated data to be effectively trained. In practical applications, obtaining high-quality labeled data is often costly, and the labeling process may contain errors. Additionally, the lack of self-supervised or unsupervised learning capabilities limits its application in scenarios where labeled data is unavailable.\n\nQUESTIONS:\nThe difference-based method proposed in the paper is highly efficient; however, it is sensitive to noise in real-world environments, especially under the influence of sensor noise, occlusion, or lighting variations. Has the performance of this method been thoroughly evaluated in noisy environments? Has the potential interference caused by noise in the difference computation, which could affect the accurate estimation of dynamic objects, been considered?\n\nWhile ∆Flow shows good generalization between Argoverse 2 and Waymo, have you tested its performance on other autonomous driving datasets, such as KITTI or nuScenes, which may have different sensor configurations and environmental conditions?\n\nAlthough the paper mentions that ∆Flow is faster in inference speed compared to other methods, it lacks a detailed quantitative analysis of inference time, memory usage, and computational resource requirements. When comparing with other methods, the paper mentions that the inference speed is faster, but does not provide specific runtime analysis.\n\nLIMITATIONS:\nThe differential method mentioned in the paper achieves improved efficiency; however, in real-world environments, especially under the influence of sensor noise and other factors, the differential method may be affected by noise. This could lead to errors in the model's dynamic object scene flow estimation, particularly when input data is of low quality or has high noise. Therefore, improving the model's robustness in noisy environments remains a challenge.\n\nThe current experiments are mainly focused on the Argoverse 2 and Waymo datasets, which are primarily from urban environments and use similar sensor configurations. While the model has achieved good results on these datasets, it has not been fully validated whether it can maintain the same performance in more complex or diverse scenarios, such as rural roads, highways, and extreme weather conditions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel method for fusing temporal features from a 3D voxel grid into one representation that can be used for scene flow estimation. Rather than fusing features by concatenating along the channel or temporal axis, DeltaFlow instead proposes using \"delta features\" which represent the difference in the features over time. This allows for a compact feature representation whose dimension doesn't scale linearly with the number of timesteps aggregated. In addition, in order to address imbalances in the dataset, two new loss terms are introduced; a category balanced loss, and an instance consistency loss.\n\nThe delta features are computed by taking the sum of N differences between the current frame and the past N frames. Previous frames are then weighted by a temporal-decay factor ($\\lambda$) in order to bias the model to the most recent observations.\n\nCategory based loss upweights the loss on certain points based on their class, which allows classes with fewer points in the dataset (such as pedestrians) have better flow estimates. Additionally, the instance loss assigns an instance to each point and ensures that flow is consistent across the instance.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\nThe main strength of DeltaFlow is the novel delta feature design which provides significant efficiency gains and propels DeltaFlow to the top of the AV2 supervised scene flow leaderboard. The ablation studies of the proposed improvements (both the delta features, and the additional loss terms) are thorough and compelling. And the visualizations provided in the appendix highlight the specific types of improvements provided by DeltaFlow over the previous supervised state of the art method (Flow4D).\n\n## Weaknesses\nThe cross-domain experiments are the weakest section of the paper. The metrics provided are underwhelming (why only report 3-way EPE when both bucket normalized and 3-way are reported everywhere else?). Similarly in other section 5.3, stable 3-way EPE numbers are used to demonstrate a preservation of static performance, but really mean 3-way EPE also includes dynamic components, and the bucketed EPE metric includes a static component itself (which is identical to that of 3-way EPE). I think that this mixing of the metrics makes some of the results a bit confusing if one isn't as familiar with the various components of the metrics and definitely the presentation of these numbers could use some clean-up. It's not bad to have more metrics, but the clarity is affected in these sections.\n\nQUESTIONS:\n## Questions\nMy main question is about the intuition of the delta feature scheme. Intuitively I understand what is being proposed, and empirically it clearly works, but the visualizations in Figure 7 don't quite line up with the intuition. There are clearly static areas which have high intensity features (likely because of differing scan patterns) and the more frames which are aggregated, the higher intensity these areas will be (because they are do not move). However the moving objects have a very distinct trail, and only exhibit high intensity features in the overlapping regions. So does this mean that if an object is moving very quickly with no overlap the feature intensity could be lower than a static region?\n\nOverall I think this figure could use some increased exposition.\n\n## Minor suggestions:\nVery minor nitpick but in Table 4, Argoverse 2 is listed as having 32 beams for its LiDAR. This is inaccurate as the point clouds from both 32 beam lidars are concatenated together giving a total of 64 beams. The cross domain generalization point still stands as obviously the sensor and scan pattern is completely different from WOD.\n\nThe NSFP listing in table 4 is also a bit confusing. Since it's a runtime optimization method, it cannot be trained on one dataset and evaluated on the other. I am assuming it's listed to serve as a baseline but this should probably be clarified.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-06"}