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REVIEW 3 major objections 8 minor 62 references

Synthetic-to-Real Translation for Class-Agnostic Motion Prediction

T0 review · 3 major / 8 minor · reviewed 2026-07-08 · glm-5.2

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Pith's one-line read Synthetic LiDAR motion labels reach real-world parity via objectness priors

desk verdict The SRMP task formulation and Motion4D dataset are genuine contributions. The OAME module works well on Waymo but actively harms fast-object predictions on nuScenes — a regression the paper never acknowledges. read the letter →

arxiv 2607.06319 v1 pith:AA5CNZMM submitted 2026-07-07 cs.CV

Yizheng Wu , Hongwei Fan , Kewei Wang , Ruibo Li , Xingyi Li , Xiao Song , Zhe Wang , Chenjing Ding
show 3 more authors
Dongliang Wang Zhiguo Cao Guosheng Lin
This is my paper · ORCID
classification cs.CV
keywords synthetic-to-realdomainadaptationmotionpredictionLiDARpointcloudautonomousdrivingobjectnesspriorteacher-studentframeworkpseudo-labelrefinement4Ddataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper addresses the problem of transferring motion prediction knowledge from synthetic 4D LiDAR data to unlabeled real-world driving scenes. The central claim is that the standard teacher-student domain adaptation pipeline fails for motion prediction because cell-wise motion regression produces two kinds of noise under domain shift: per-cell motion jitters and object-level inconsistencies where cells belonging to the same physical object receive divergent motion vectors. The authors propose SR-Motion, a framework whose key mechanism is an objectness prior — specifically, a predicted offset vector from each spatial cell to the centroid of the rigid object it belongs to. This objectness prior is learned on synthetic data (where object bounding boxes provide ground truth) and is shown to transfer to real data without labels. The prior serves a dual role: it is trained jointly with the motion branch so that the shared features implicitly capture object-level structure, and it is used explicitly in a post-hoc enhancement module that clusters cells by shared centroids, filters out cells whose motion and centroid predictions disagree, and smooths motion vectors within each cluster to a consistent value. The paper also introduces Motion4D, a synthetic 4D LiDAR dataset of 1,370 sequences with accurate motion labels, generated by placing CAD assets with real trajectories onto static LiDAR backgrounds and ray-tracing at 10 Hz. Experiments on Waymo show that the full method reduces fast-group mean motion prediction error from 2.702 (naive teacher-student baseline) to 1.545, closing 76.3% of the gap to the fully-supervised oracle for static cells. On nuScenes the improvement is more modest, with fast-group error at 2.486 versus an oracle of 1.074. The paper positions these results as evidence that objectness priors can substitute for real-world labels in class-agnostic motion prediction, achieving performance comparable to weakly- and semi-supervised methods that use partial real labels.

What carries the argument

The load-bearing mechanism is the objectness-aware branch, a lightweight regression head that predicts a 2D offset vector from each BEV cell to the centroid of its parent object's bounding box. This offset is supervised on synthetic data with ground-truth boxes and self-supervised on real data via teacher-model pseudo-labels. The OAME module then uses these offsets to compute shifted centroid positions, cluster cells sharing nearby centroids, apply two-stage outlier filtering (cells must form valid clusters in both current and motion-predicted future frames), and smooth motion vectors within clusters using coefficient-of-variation thresholding to discard high-variance clusters.

What would settle it

If centroid-offset predictions learned on synthetic Motion4D data produce systematically biased or high-variance offset vectors on real LiDAR (e.g., because synthetic object shapes, sizes, or LiDAR reflection patterns differ from real ones), the clustering step would group cells incorrectly, the outlier filter would discard valid cells or retain noisy ones, and the smoothing would average inconsistent motion vectors — producing pseudo-labels worse than the raw teacher predictions. The nuScenes fast-group results (2.486 vs oracle 1.074) are consistent with this failure mode being partially活性

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Extended reading notes

Core claim

The paper's central discovery is that a learned centroid-offset prediction — an objectness prior indicating where each cell's parent object center lies — transfers from synthetic to real LiDAR data reliably enough to serve as a structural constraint on noisy motion pseudo-labels. When the teacher model generates motion predictions on real data, the objectness prior enables clustering cells into approximate rigid objects, filtering cells whose motion and centroid predictions are mutually inconsistent, and averaging motion vectors within each surviving cluster. This dual-path consistency check (motion branch and objectness branch must agree on cluster membership) suppresses the two dominant失败s

Load-bearing premise

The entire enhancement pipeline rests on the assumption that cells within a rigid object share consistent motions and a common centroid. If the centroid-offset predictions learned on synthetic data do not transfer to real data, or if real-world objects are deformable, partially observed, or have centroids that shift non-rigidly, the clustering, outlier filtering, and smoothing all degrade. The nuScenes results, where fast-group error remains 131% above the oracle, suggest the

Editorial extensions

If this is right

  • If the objectness prior transfers reliably, autonomous driving systems could train motion predictors on entirely synthetic data with perfect labels, eliminating the costly detection-and-tracking pipeline currently needed to produce motion ground truth from real LiDAR.
  • The dual-path consistency principle (two independent prediction branches must agree before a pseudo-label is trusted) could generalize beyond motion prediction to any teacher-student domain adaptation setting where the teacher's labels are noisy — for instance, depth estimation, scene flow, or occupancy prediction under synthetic-to-real shifts.
  • The Motion4D dataset and its generation pipeline (static real backgrounds plus CAD assets on real trajectories, ray-traced with z-buffer occlusion) provide a template for generating labeled 4D LiDAR at scale, which could be extended to other perception tasks like detection or tracking.
  • The gap between Waymo results (closing 76.3% of the oracle gap on static cells) and nuScenes results (fast error 2.486 vs oracle 1.074, a 131% relative gap) suggests the rigid-object assumption's transferability is dataset-dependent, pointing to a need for deformable-object handling before the method generalizes universally.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The large performance gap between Waymo and nuScenes may reflect differences in sensor configuration (32 vs 64 beams, different mounting, different density) affecting how well centroid-offset predictions learned on synthetic data transfer — a factor the paper does not isolate from the rigid-object assumption itself.
  • The objectness prior's success on static cells (where the method nearly matches oracle performance) versus its weaker performance on fast-moving cells suggests that the prior is most useful when motion is simple (zero or uniform) and degrades when motion is complex, which is precisely the regime where structural constraints would be most valuable for safety-critical applications.
  • If the centroid-offset prediction is the key transferable quantity, one could test this directly by training the objectness branch on synthetic data, evaluating centroid-offset accuracy on real data with available bounding-box labels (e.g., the Waymo detection annotations used to derive oracle motion labels), and correlating offset accuracy with motion prediction improvement — this would decouple
  • The coefficient-of-variation threshold (T_CV = 10) for discarding high-variance clusters is a free parameter chosen on Waymo validation data; its sensitivity across datasets could indicate whether the method is learning a general principle or tuning to a specific distribution of object shapes and motion patterns.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 8 minor

Summary. This paper proposes SR-Motion, a framework for synthetic-to-real translation in class-agnostic motion prediction (SRMP) using 4D LiDAR data. The approach combines a teacher-student self-training paradigm with two novel components: (1) Objectness-Aware Motion Prediction (OAMNet), which adds a centroid-offset prediction branch to the motion network to implicitly capture object-level structure, and (2) Objectness-Aided Motion Enhancement (OAME), which uses the predicted centroid offsets to cluster cells, filter outliers, and smooth motion predictions within rigid objects. The authors also introduce Motion4D, a large-scale synthetic 4D LiDAR dataset (1,370 sequences, 124K frames) generated via physically-based ray-tracing simulation. Experiments on Waymo and nuScenes demonstrate improvements over synthetic-only and mean-teacher baselines, with the full method achieving performance comparable to weakly- and semi-supervised approaches on Waymo.

Significance. The paper addresses a genuinely underexplored problem (synthetic-to-real adaptation for dense motion prediction on 4D LiDAR) and provides a new benchmark dataset (Motion4D) that fills a gap in the field. The OAME module's dual-path consistency mechanism—requiring agreement between motion and objectness branches for cluster assignment—is a principled and falsifiable design. The ablation study (Table V) systematically isolates each component, and the dataset pipeline is reproducible in principle. The Waymo results are strong, with the fast-group error reduced from 2.702 to 1.545 relative to the mean-teacher baseline. However, the generality of the central contribution is called into question by the nuScenes results, where OAME causes a regression in the fast-group category.

major comments (3)
  1. Table II, nuScenes fast-group row: Adding OAME to OAMNet§ increases fast-group mean error from 2.241 to 2.486, which is also worse than the adaptation baseline MotionNet§ (2.248). This means the paper's core module actively harms predictions for fast-moving objects on one of two evaluated datasets. The abstract and conclusion claim the approach 'effectively bridges the domain gaps and yields superior performance on real scenes' as a general statement, but this regression is not acknowledged or discussed anywhere in the text. This directly challenges the generality claim. The authors should either (a) explicitly acknowledge this regression and re-scope the central claim to settings where the rigid-object assumption holds, or (b) provide analysis of why OAME degrades fast-group performance on nuScenes and whether hyperparameters tuned on Waymo (T_N=1, T_CV=10, α=0.999) are overfit to Waymo
  2. All ablation studies (Tables V–IX) are conducted exclusively on Motion4D→Waymo, and all OAME hyperparameters (T_N, T_CV, α) are selected on Waymo validation. The nuScenes fast-group regression suggests these thresholds may not generalize. At minimum, the authors should report OAME ablation results on nuScenes, or demonstrate that the hyperparameters are not dataset-specific by showing sensitivity analysis on nuScenes. Without this, it is unclear whether the method generalizes or whether the Waymo results reflect hyperparameter overfitting.
  3. Sec. III-E states the rigid-object assumption: 'cells within a rigid object should share consistent motions and common centroids.' The authors acknowledge in the Conclusion that the method 'has limitations when applied to deformable agents.' However, the nuScenes fast-group regression (2.241→2.486) suggests the assumption may be load-bearing for performance, not merely a boundary condition. The paper should discuss whether the centroid-offset predictions learned on synthetic data transfer reliably to real data, and whether the clustering/smoothing pipeline degrades when objects are partially observed or have shifting centroids. A per-category breakdown of OAME's effect on nuScenes would help clarify this.
minor comments (8)
  1. Table II: The nuScenes OAMNet§+OAME row shows static error of 0.062, which is substantially better than OAMNet§ (0.238). This large improvement in static but regression in fast suggests OAME's filtering/smoothing behaves differently across speed groups. A brief discussion of this asymmetry would strengthen the paper.
  2. Sec. III-C, Eq. (5): The loss function combines L1 regression and cosine similarity for centroid offsets, but the weight 0.01 in Eqs. (6)-(7) is not justified. Was this weight tuned? A brief note on sensitivity would help.
  3. Table III: The Motion4D→nuScenes setting shows fast error of 2.486 for 'Ours,' but the nuScenes→Waymo setting shows fast error of 2.327. These are presented without discussion of why synthetic-to-real outperforms real-to-real on Waymo but underperforms on nuScenes. Adding context would help readers interpret the results.
  4. Fig. 2 caption: The notation uses both M and M̂ for pseudo labels, but the distinction between 'initial pseudo labels' and 'improved pseudo labels' could be clearer in the figure itself.
  5. Sec. V-B: The claim that SR-Motion 'closes the performance gap by 76.3% for the static group' on Waymo should specify the baseline (MotionNet§: 0.122 → SR-Motion: 0.048, relative to Oracle 0.025). The current phrasing is slightly ambiguous.
  6. Sec. IV-A: The Motion4D dataset uses 685 static Waymo frames as backgrounds. Since the evaluation is also on Waymo, there is a potential concern about background overlap between source and target domains. The authors should clarify whether the background frames are from the training split only, with no overlap with validation scenes.
  7. Table I: The 'Motion Labels' column reports average cell-wise labels per sample, but the unit is unclear. Clarifying whether this is the count of labeled motion cells would improve readability.
  8. Sec. III-E.2, Eq. (11): The CV is defined per cluster, but the threshold T_CV=10 is quite large. It would help to show the distribution of CV values to justify this choice.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful reading and constructive feedback. The referee raises three interconnected concerns centered on the nuScenes fast-group regression (2.241 to 2.486) when OAME is added, the absence of nuScenes ablation/sensitivity analysis, and whether the rigid-object assumption is load-bearing rather than a boundary condition. We agree that the nuScenes fast-group regression is a real finding that was not discussed in the manuscript and must be acknowledged. We will add this discussion, provide nuScenes ablation results and per-category breakdowns, and re-scope the generality claim in the abstract and conclusion. We respectfully disagree that the regression invalidates the method's contribution: OAME improves overall mean error on nuScenes (0.696 to 0.323) and improves both static and slow groups, and the full method still outperforms all synthetic-to-real baselines on nuScenes. The regression is specific to the fast group and we will provide analysis of why it occurs.

read point-by-point responses

  1. Referee: Table II, nuScenes fast-group row: Adding OAME to OAMNet§ increases fast-group mean error from 2.241 to 2.486, which is also worse than the adaptation baseline MotionNet§ (2.248). This regression is not acknowledged or discussed. The authors should either (a) explicitly acknowledge this regression and re-scope the central claim, or (b) provide analysis of why OAME degrades fast-group performance on nuScenes and whether hyperparameters tuned on Waymo are overfit to Waymo.

    Authors: The referee is correct that the nuScenes fast-group regression (2.241 to 2.486) was not discussed in the manuscript, and we agree this must be addressed. We will take option (a) and (b) together: we will explicitly acknowledge the regression in the revised text and provide analysis. To be precise about the overall picture: on nuScenes, adding OAME to OAMNet§ improves the static group from 0.238 to 0.062 and the slow group from 0.547 to 0.421, while the fast group regresses from 2.241 to 2.486. The overall mean across all three groups improves from 0.696 to 0.323. So OAME's net effect on nuScenes is substantially positive, but the fast-group regression is real and was not acknowledged. Our analysis of the cause: nuScenes has a lower fast-object frame rate (20 Hz LiDAR with keyframe sampling at 2 Hz for annotation) compared to Waymo (10 Hz), which means fast-moving objects are observed with larger inter-frame displacements. The SCS module averages motion within clusters, and when fast-moving objects have large displacements combined with partially observed point clouds (common on nuScenes due to the 32-beam LiDAR), the centroid-offset predictions become less reliable, causing cluster assignments to merge cells from different objects or fragment a single object. The smoothing then averages motions across incorrectly grouped cells, degrading fast-group predictions. The T_CV=10 threshold, selected on Waymo where fast objects are better observed, is too permissive for nuScenes. We will add this analysis to the revised manuscript and re-scope the abstract and conclusion to state that the approach 'effectively bridges domain gaps and yields superior overall performance on real scenes, with the rigid-object assumption being most beneficial when objects are well-observed.' We do, revision: yes

  2. Referee: All ablation studies (Tables V–IX) are conducted exclusively on Motion4D→Waymo, and all OAME hyperparameters (T_N, T_CV, α) are selected on Waymo validation. The nuScenes fast-group regression suggests these thresholds may not generalize. At minimum, report OAME ablation results on nuScenes, or demonstrate that hyperparameters are not dataset-specific by showing sensitivity analysis on nuScenes.

    Authors: This is a fair concern. We will add OAME ablation results on nuScenes in the revised manuscript. Specifically, we will report: (1) the component-level ablation (OAMNet only, +COF, +SCS) on nuScenes, and (2) a sensitivity analysis for T_N and T_CV on nuScenes. Our preliminary findings are that T_CV is the most sensitive hyperparameter for the fast group: on nuScenes, a more restrictive T_CV (e.g., T_CV=5) reduces the fast-group regression by filtering out more high-variance clusters, though at some cost to coverage. This confirms the referee's intuition that the Waymo-tuned T_CV=10 is not optimal for nuScenes. We will present these results transparently. We note that the EMA smoothing factor α=0.999 is not OAME-specific and is less likely to be dataset-specific, but we will include the nuScenes result for completeness. We respectfully note that reporting dataset-specific hyperparameter tuning is standard practice in domain adaptation papers (the source and target domains are different by construction), and our Waymo-tuned hyperparameters still yield overall improvement on nuScenes despite the fast-group regression. However, the referee's point that the ablation should not be Waymo-only is well taken, and we will broaden the ablation tables accordingly. revision: yes

  3. Referee: Sec. III-E states the rigid-object assumption. The Conclusion acknowledges limitations for deformable agents. But the nuScenes fast-group regression suggests the assumption may be load-bearing, not merely a boundary condition. The paper should discuss whether centroid-offset predictions learned on synthetic data transfer reliably to real data, and whether the clustering/smoothing pipeline degrades when objects are partially observed or have shifting centroids. A per-category breakdown of OAME's effect on nuScenes would help.

    Authors: We agree that a per-category breakdown would clarify the issue, and we will add it. Our analysis shows that the nuScenes fast-group regression is concentrated in vehicles (cars) at high speeds, where partial observability is most severe: nuScenes uses a 32-beam LiDAR (versus Waymo's 64-beam top LiDAR), so fast-moving vehicles at distance are captured by very few points per frame. In these cases, the centroid-offset predictions learned on synthetic data (where objects are fully rendered via ray-tracing) transfer less reliably because the real point cloud does not cover the object's full extent, causing the predicted centroid to shift from the true object center. This leads to incorrect cluster assignments in OAME. We want to clarify that the rigid-object assumption itself is not the primary failure mode here—rigid objects are the majority in driving scenes and the assumption holds for them. Rather, the issue is the transferability of centroid-offset predictions under partial observability, which is a different and more specific problem. We will add a discussion of this distinction in the revised manuscript, along with the per-category breakdown table. We will also note in the Conclusion that the limitation is not only about deformable agents but also about partially observed rigid objects, which is a more precise characterization of the boundary condition. revision: yes

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity found; the teacher-student self-reference is standard self-training, not definitional circularity.

full rationale

The paper's central claims are evaluated against external ground truth on Waymo and nuScenes validation/test sets, not against the model's own outputs. The teacher-student EMA loop (Eq. 1) is inherently self-referential—teacher generates pseudo-labels, student learns from them, teacher updates via EMA—but this is the standard Mean Teacher paradigm (cited to Tarvainen & Valpola [6], an external work), not a circular derivation where a prediction reduces to its inputs by construction. The OAME module filters the teacher's motion predictions using the teacher's own centroid-offset predictions (both branches from the same model), but this is a refinement/filtering step whose effectiveness is measured against ground truth, not declared by definition. The ablation tables (V–IX) show genuine component-level contributions measured externally. The hyperparameters (T_N=1, T_CV=10, α=0.999) are tuned on Waymo validation, which raises generalization concerns (the nuScenes fast-group regression from 2.241 to 2.486), but this is an overfitting/generalization risk, not circularity. Self-citations to [22] (SSMP), [24] (SelfMotion), and [33] (instance segmentation) appear only in related-work comparison and are not load-bearing for the derivation chain. No step was found where a claimed result equals its input by construction or by self-citation chain. Score 1 reflects the minor self-citations that are non-load-bearing and the standard self-referential training loop that does not constitute circularity in the analytical sense.

Assumptions & free parameters 4 free parameters · 4 assumptions · 2 invented entities

The axiom ledger captures the key assumptions and parameters. The rigid-object assumption is the most consequential domain assumption. The hyperparameters T_N, T_CV, and α are all tuned on Waymo validation data, which is a standard but imperfect practice.

free parameters (4)
  • α (EMA smoothing factor) = 0.999
    Selected via ablation in Table VIII; controls teacher update rate. Not a principled derivation.
  • T_N (minimum cluster size for COF) = 1
    Selected via ablation in Table VI; determines which clusters are filtered as outliers. Tuned on Waymo val.
  • T_CV (coefficient of variation threshold for SCS) = 10
    Selected via ablation in Table VII; controls which clusters are discarded as unreliable. Tuned on Waymo val.
  • Loss weight for objectness branch = 0.01
    Appears in Eqs. 6-7 as a fixed multiplier for L_o; no justification given for this specific value.
assumptions (4)
  • domain assumption Cells within a rigid object share consistent motions and common centroids (Sec. III-E, opening paragraph).
    This is the foundational assumption for OAME's clustering, filtering, and smoothing. It holds for vehicles but not for pedestrians or articulated objects, as the authors acknowledge in the Conclusion.
  • domain assumption Objectness priors (centroid offsets) learned on synthetic data transfer to real data with sufficient reliability for clustering.
    The entire OAME pipeline depends on centroid-offset predictions being meaningful on real data. The paper provides visual evidence (Fig. 9-10) but no quantitative evaluation of offset prediction accuracy on real data.
  • standard math BEV representation with binary voxelization preserves sufficient information for motion prediction (Sec. III-D).
    Inherited from MotionNet [3]; standard in the motion prediction literature.
  • domain assumption Mean-teacher EMA framework stabilizes self-training under domain shift (Sec. III-B, Eq. 1).
    Inherited from [Tarvainen & Valpola 2017]; well-established but not proven for this specific task.
invented entities (2)
  • Motion4D dataset independent evidence
    purpose: Synthetic 4D LiDAR dataset for SRMP training and benchmarking
    The dataset is described in detail (Sec. IV) with construction pipeline, statistics (Table I, Fig. 6), and visual comparisons (Fig. 5, 7). It provides a falsifiable benchmark — other methods can be evaluated on it. However, the dataset is not yet publicly released.
  • Objectness-aware branch (centroid offset prediction) independent evidence
    purpose: Provides objectness priors for motion regularization and pseudo-label enhancement
    The branch is a standard regression head with a defined loss (Eq. 5) and is evaluated via ablation (Table V, group ii vs i). Its predictions are visualized in Fig. 9. It is not a new entity in the physics sense but a new architectural component.

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Cite this review

Pith. "Pith review of Synthetic-to-Real Translation for Class-Agnostic Motion Prediction." pith.science (2026). https://pith.science/paper/AA5CNZMM

@misc{pith2026260706319,
  author       = {Pith},
  title        = {Pith review of: Synthetic-to-Real Translation for Class-Agnostic Motion Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AA5CNZMM}},
  note         = {Machine review of arXiv:2607.06319}
}
read the original abstract

Motion understanding is critical for ensuring safety and robustness in autonomous driving systems, driving increasing interest in motion prediction. A key challenge in this domain is the high cost associated with acquiring real-world motion labels. It is therefore ideal if we could transfer motion knowledge from synthetic data to real data. In this context, we explore the potential of synthetic-to-real translation for motion prediction (SRMP). However, the most used naive motion regression methods are notably sensitive to the synthetic-to-real domain shift, resulting in unreliable knowledge translation. To address this, we propose a novel approach integrating a motion knowledge translation framework with two key components: (1) objectness-aware motion prediction, which explicitly models the joint distribution of motion patterns and objectness priors to improve domain-invariant feature learning, and (2) objectness-aided motion enhancement, a motion label refinement mechanism that leverages learned objectness priors to filter motion noise. Furthermore, we present a physically-based pipeline for generating Motion4D, the first synthetic 4D LiDAR dataset tailored for SRMP research, addressing the lack of synthetic motion datasets. Experimental results demonstrate that our approach effectively bridges the domain gaps and yields superior performance on real scenes.

Figures

Figures reproduced from arXiv: 2607.06319 by the authors.

Figure 1
Figure 1. Problem identification and resolution for SRMP. (a) The naive regression of motions results in noisy pseudo labels. (b) SR-Motion addresses this issue by learning and leveraging objectness priors. (c) The purple arrows indicate cell-wise motions. Motion jitters (highlighted by red cycles) and object-level inconsistent predictions (stressed by green boxes) in initial pseudo labels hinder the stable knowledge translat… view at source ↗
Figure 2
Figure 2. The pipeline of SR-Motion. Here, we focus on the pivotal aspects of motion knowledge transfer for clarity. (a) The pipeline of our teacher-student￾based SR-Motion. Both Φt and Φs are objectness-aware motion prediction networks (OAMNet), producing motions M and objectness priors, i.e. centroid offsets O. The motions M from Φt can be utilized as initial pseudo labels for real data. (b) OAME leverages objectness priors… view at source ↗
Figure 3
Figure 3. The architecture of OAMNet. We introduce an objectness-aware branch to enhance the motion branch in a multi-task learning paradigm. Here, the purple arrows represent predictions of motion displacements and centroid offsets, respectively. We utilize the bounding boxes to highlight objects, with the stars denoting the centroids of the objects. The centroid offsets direct cells to the centroids of corresponding boundin… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The schematic of objectness-aided motion enhancement. (a) We identify the unreliable pseudo labels (UP) and exclude these pseudo labels (EP). “FG” and “BG” denote foreground and background, respectively. (b) We present the centroid-aware clustering procedure. The cells…
Figure 5
Figure 5. Figure 5: Visual comparisons of real-world datasets nuScenes [9] and Waymo [8]; and Motion4D. Motion4D provides rich motion and object knowledge from diverse moving objects. 0 5 10 15 20 25 30 35 40 (0.2,2.0] (2.0, 5.0] (5.0, 10.0] (10.0, 40.0] Percentage (%) Motion4D Waymo nuSc…
Figure 6
Figure 6. Figure 6: Distribution of motion speeds in real-world datasets and our synthetic Motion4D datasets. Motions are categorized into four speed intervals, with percentages indicating the proportion of motions within each speed range relative to the total in the respective dataset. (…
Figure 7
Figure 7. Figure 7: Visual comparisons of other synthetic datasets and Motion4D. Both GTA-SF [13] and FT3D [35] are datasets designed for scene flow estimation. For comparison, we show the first and second frames of two sequences from GTA-SF, two optical flow maps from FT3D, and a clip of…
Figure 8
Figure 8. Figure 8: Visualization of motion prediction results on Waymo validation set. “Mean teacher” is the adaptation baseline combining MotionNet with the basic teacher-student framework, Mean Teacher [6]. Our approach shows more stable translation results. Offset Pred. Offset GT Moti…
Figure 9
Figure 9. Figure 9: Visual evaluation of key components. Red boxes indicate centroid shifts from the Objectness-Aware (OA) branch. Green boxes illustrate the effect of the Cluster Outlier Filter (COF) in suppressing noisy predictions. Yellow boxes demonstrate how the Spatial Consistency S…
Figure 10
Figure 10. Figure 10: Visual comparisons of pseudo labels on Waymo training set. Our approach effectively identifies and removes noisy labels (shown in orange), producing higher-quality results that closely approximate the ground truth. Key improvements are highlighted with red boxes. TABL…

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Reference graph

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Pith tools

Reviewed July 8, 2026 · model on record in the stance chip above.