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SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis

Jipeng Lyu, Jiahua Dong, Yu-Xiong Wang

arXiv:2510.06694v1cs.CV

TL;DR

Dynamic scene modeling must capture deformations efficiently while supporting tracking and novel-view synthesis. SCas4D uses cascaded, coarse-to-fine Gaussian deformation, reporting convergence within 100 iterations and competitive quality with one-twentieth the training iterations.

  • Problem

    Dynamic scene modeling must capture accurate deformations efficiently for tracking and novel-view synthesis, while NeRF’s computational costs and implicit representation limit efficiency and some downstream tasks.

    Source: §1 · p. 2

  • Method

    SCas4D organizes Gaussians into cascaded levels, refining deformations from parts to points with functions that capture rotation, translation, and scaling.

    Source: §1 · p. 2, §3.3 · p. 6

  • Results

    SCas4D reports convergence within 100 iterations and competitive quality with 1/20th of the training iterations, alongside strong novel-view synthesis and dense point tracking.

    Source: §1 · p. 2, §5 · p. 11

  • Takeaways & Limitations

    The approach demonstrates self-supervised articulated-object segmentation alongside novel-view synthesis and dense point tracking.

    Source: §5 · p. 11

    Part-level modeling assumes small object chunks are nearly rigid, while KMeans grouping assumes same-part Gaussians remain close and share rotations.

    Source: §3.2 · p. 5, §4.3 · p. 10

Abstract

from arXiv · show

Persistent dynamic scene modeling for tracking and novel-view synthesis remains challenging due to the difficulty of capturing accurate deformations while maintaining computational efficiency. We propose SCas4D, a cascaded optimization framework that leverages structural patterns in 3D Gaussian Splatting for dynamic scenes. The key idea is that real-world deformations often exhibit hierarchical patterns, where groups of Gaussians share similar transformations. By progressively refining deformations from coarse part-level to fine point-level, SCas4D achieves convergence within 100 iterations per time frame and produces results comparable to existing methods with only one-twentieth of the training iterations. The approach also demonstrates effectiveness in self-supervised articulated object segmentation, novel view synthesis, and dense point tracking tasks.

1 Introduction

Dynamic scene methods face efficiency and representation challenges, while Gaussian-level updates underuse shared motion structure. SCas4D applies cascaded structural optimization and reports faster training with competitive tracking and articulated segmentation.

1 Introduction

  • Dynamic-scene NeRF methods face high query and rendering costs, while implicit representations restrict precise tracking and articulated-object segmentation.Existing Gaussian methods learn per-Gaussian transformations but underexplore structural information; sparse control points may miss accurate dense tracking and require suitable placement.Source: §1 · p. 2
  • SCas4D progressively optimizes 3D parts and then individual Gaussians, using three levels to balance efficiency with fine-grained motion capture.Its multi-level deformation functions capture rotation, translation, and scaling.Source: §1 · p. 2
  • 20× speedup over Dynamic3DGS in training time accompanies comparable dense-point tracking and articulated-object segmentation.The paper also reports competitive novel-view rendering and high-quality articulated-object segmentation.Source: §1 · p. 2
  • The paper explores 3D Gaussian Splatting’s internal structure for dynamic scenes and reports competitive novel-view rendering and point tracking.It also proposes articulated-object segmentation for 3DGS.Source: §1 · p. 2

2 Related Works

Prior dynamic-scene work spans NeRF-based methods, 3D Gaussian Splatting, and online reconstruction, with trade-offs in efficiency, generality, and tracking. SCas4D targets online scenes with clustered, coarse-to-fine deformation while retaining per-Gaussian detail.

2 Related Works

  • NeRF-based static-scene methods face costly neural networks and volume rendering, while 3D Gaussian Splatting uses explicit representation and differentiable rasterization.The paper positions its work as extending static 3D Gaussians to dynamic scenes.Source: §2 · p. 3
  • Dynamic-scene methods use scenario-specific priors, canonical deformation, or dynamic Gaussian representations, but face limits in generality, complex motion, efficiency, or tracking.3DGStream’s pruning prevents point tracking across all frames, and SC-GS struggles with accurate per-point tracking and depends on control-point distribution.Source: §2 · pp. 3–4
  • SCas4D targets online scenes with clustered coarse-to-fine deformation, grouping similar motions while preserving full per-Gaussian update granularity.Figure 2 depicts previous-frame Gaussians and new frame inputs feeding cascaded clusters whose deformation functions are nested for each Gaussian.Source: Figure 2 · p. 3, §2 · p. 4
  • Online dynamic-scene datasets need multi-view inputs for each frame, and offline datasets or small-motion sequences may not fit the evaluation setting.The paper selects accelerated datasets from Abou-Chakra et al. (2024) and Luiten et al. (2024).Source: §2 · p. 4

3 Method

SCas4D reconstructs dynamic scenes online, using each frame’s images and the previous frame’s Gaussians. Its deformation model moves from coarse cluster transformations to fine Gaussian-level adjustments.

3.1 Preliminary

  • For online reconstruction, the method predicts deformation between consecutive frames and uses the learned function to update Gaussian centroids and covariance matrices.The deformation function is learned from the previous-frame reconstruction and current-frame observations.Source: §3.1 · p. 5

3.2 Single-layer Deformation Function

  • A cluster’s deformation combines rotation, translation, and position-dependent scaling, with the scaling factor constrained to (0, 2).The scaling uses a trainable linear function of the point’s local coordinate, passed through tanh.Source: Figure 3 · p. 5, §3.2 · p. 6

3.3 Cascaded Structural Optimization Strategy

  • Coarse-to-fine clustering balances speed and deformation detail: broad group transformations provide a better starting point for finer clusters, which refine motion.The method merges neighboring centroid-based K-means clusters into coarser layers and applies their deformation functions in sequence; it also adds per-Gaussian centroid, rotation, and scale deltas.Source: §3.3 · p. 6

3.4 Optimization Process and Training Strategies

  • Each new frame is optimized from the previous frame’s Gaussians and current images, with backpropagation of 2D loss updating deformation parameters.The pipeline begins with static-scene reconstruction and coarse-to-fine clustering; clustering can be updated mid-training if the scene changes significantly.Source: §3 · p. 4, §3.4 · p. 6
  • Training combines image losses with local-rigidity, isometry, rotation, and scale losses; the scale loss discourages excessively large or elongated Gaussians.The implementation fixes opacity and background logits after static training, keeps color trainable, and uses trainable 3D RGB vectors with a soft RGB loss.Source: §3.4 · pp. 6–7
  • Figure 4 presents FastParticle rendering comparisons after 100 iterations per frame, while Figure 5 shows articulated-object segmentation results.The captions identify the visualizations but do not specify their comparative outcomes.Source: Figure 4 · p. 8, Figure 5 · p. 8

4 Experiments

Experiments on challenging real and synthetic scenes compare rendering, tracking, and segmentation, with SCas4D reporting strong results at substantially fewer iterations. Ablations examine convergence, clustering depth, and covariance coupling.

4.1 Dataset Preparation

  • Evaluation uses six real Panoptic scenes and six synthetic FastParticle scenes, including accelerated motion to test rapid convergence under challenging dynamics.Source: §4.1 · p. 8

4.2 Comparisons

  • With the same static checkpoints on FastParticle and Panoptic, our method achieves results comparable to Dynamic3DGS at 2,000 iterations using 100 iterations between frames, and far surpasses it at 100 iterations.Source: §4.2 · p. 8
  • The paper’s method achieved the best results across both datasets against four methods; online methods trained 100 iterations per frame, while offline methods received 100 times the total frame count in iterations.All methods used the same static-scene checkpoints. SC-GS used 10,000 iterations to establish control points; results distinguish pretraining from no-pretraining.Source: §4.2 · pp. 8–9
  • At equal training iterations, SCas4D significantly outperforms Dynamic3DGS in normalized 2D MTE across scenes; Wheel remains ambiguous under strong symmetry.The tracking figure compares predicted tracks with ground truth.Source: Figure 6 · p. 9, §4.2 · p. 10

4.3 Visualization of Part Segmentation

  • K-means on per-Gaussian position-and-rotation trajectories yields intuitive part segmentations on synthetic and real scenes without semantic labels.Each Gaussian's clustering feature concatenates its positions, flattened rotations, and repeated initial position across time.Source: §4.3 · p. 10

4.4 Ablation Study

  • On FastParticle, the authors’ method consistently outperforms Dynamic3DGS at matched training iterations, is very close to convergence after 100 iterations, and reaches the same convergence point as Dynamic3DGS after 2,000 iterations.Source: Figure 8 · p. 9, §4.4 · p. 10
  • Three clustering layers outperform one across FastParticle metrics and scenes; four or five offer no further improvement and may slow convergence.The study identifies K = 3 as a balance between efficiency and effectiveness.Source: Table 4 · p. 9, §4.4 · p. 11
  • Coupling covariance to centroid deformation aligns Gaussian rotations and scales with wheel motion; uncoupled Gaussians render suboptimally despite correctly learned positions.Figure 7 presents the entangled-covariance ablation.Source: Figure 7 · p. 9, §4.4 · p. 11

5 Conclusion

The conclusion presents SCas4D as a cascaded framework for dynamic-scene tracking and novel-view synthesis, using hierarchical deformations to refine motion from parts to points.

5 Conclusion

  • SCas4D refines deformations from part-level to point-level, converging within 100 iterations while maintaining competitive quality with one-twentieth of the training iterations.The conclusion also describes the framework as focusing on efficient tracking and novel-view synthesis.Source: §5 · p. 11
  • The method demonstrates effectiveness in self-supervised articulated object segmentation, novel-view synthesis, and dense point tracking.The conclusion identifies potential for further improvement through other 3D Gaussian Splatting variants.Source: §5 · p. 11
  • The conclusion reports strong performance in novel-view synthesis and dense point tracking, alongside its efficiency focus.Source: §5 · p. 11

A Appendix

The appendix adds dataset context and further evidence and implementation details for SCas4D, including segmentation results, comparisons, clustering, tracking labels, and deformation learning.

A Appendix

  • The appendix provides additional details about the FastParticle and Panoptic datasets used in the experiments.Source: A Appendix · p. 15
  • It presents full qualitative results for articulated-object segmentation and a comparison under equal wall-clock time, where SCas4D still outperforms Dynamic3DGS Luiten et al. (2024).It also explains why comparisons maintain the same number of iterations.Source: A Appendix · p. 15
  • The appendix visualizes the multi-layer clustering structure and manually annotated tracking labels used to evaluate 2D tracking results.Source: A Appendix · p. 15
  • It discusses the two-phase training strategy for learning deformation and reflects on limitations and future improvement areas.Source: A Appendix · p. 15

A.1 FastParticle and Panoptic Datasets

The appendix describes the real-world Panoptic and accelerated synthetic FastParticle datasets, including their scene, frame, and camera splits, and the static checkpoints used across baselines.

A.1 FastParticle and Panoptic Datasets

  • Panoptic contains six real-world scenes with 150 frames each and 31 cameras per scene, split into 27 training and 4 testing cameras.The scenes are Basketball, Boxes, Football, Juggle, Softball, and Tennis; each frame has segmentation, and background and floor losses are used following Luiten et al. (2024).Source: §A.1 · p. 15
  • Accelerated FastParticle contains six synthetic scenes with 18–38 frames each, using 36 of 40 cameras for training and 4 randomly selected cameras for testing.The scene frame counts are Robot 35, Spring 18, Wheel 38, Pendulums 24, Robot-Task 35, and Cloth 35; the scenes show significant changes between frames.Source: §A.1 · p. 15
  • All baselines receive the same static checkpoints, trained for 20,000 iterations across the 12 scenes; most FastParticle scenes need only 3,000 iterations.The passage attributes the variation to differences in static-scene complexity.Source: §A.1 · p. 15

A.2 Articulated Objects Segmentation

The segmentation appendix motivates clustering Gaussians by their shared rigid-part motion: points in one part remain close over time, and Gaussians in a rigid part share a rotation.

A.2 Articulated Objects Segmentation

  • Gaussians in the same object part are expected to remain close over time and share a rotation, motivating the KMeans design.Source: §A.2 · p. 15
  • Figure 9 illustrates the changing dynamics of three FastParticle scenes, while Figures 11 and 12 show qualitative results on FastParticle and Panoptic.Source: Figure 9 · p. 16, Figure 11 · p. 17, Figure 12 · p. 18
  • A rigid body's constituent parts rotate by the same R about their own centroids, so Gaussians in the same rigid body should share a rotation.The appendix generalizes the argument from a two-part division to multiple parts.Source: §A.2 · p. 16, Figure 10 · p. 16
  • The qualitative comparison labels identify Dynamic3DGS, Ours, and Ground Truth, with examples labeled for Robot-Task, Spring, Pendulums, Wheel, Basketball, Juggle, Boxes, Softball, and Tennis.Source: §A.2 · pp. 17–18
  • Table 5 compares the method trained with 100 iterations per time frame against Dynamic3DGS, while Table 6 reports PSNR for cluster-layer ablations.Source: Table 5 · p. 19, Table 6 · p. 19

A.3 Full Qualitative Results

The appendix presents qualitative results for all 12 scenes across two datasets, comparing the method with Luiten et al. (2024) at 100 iterations between consecutive frames.

A.3 Full Qualitative Results

  • Qualitative results cover all 12 scenes from the two datasets.The comparisons are shown in Figures 11 and 12.Source: §A.3 · p. 19
  • Both methods are trained for 100 iterations between consecutive frames.Source: §A.3 · p. 19
  • The qualitative comparison is against Luiten et al. (2024).Source: §A.3 · p. 19

A.4 Same Wall-clock Time Comparisons

At comparable wall-clock time, SCas4D uses fewer iterations yet reports better efficiency and final performance than Dynamic3DGS; the paper also explains how iteration counts indicate convergence speed.

A.4 Same Wall-clock Time Comparisons

  • The experiments use equal iteration counts across methods because iteration count reflects convergence speed, while wall-clock time can depend on implementation choices such as CUDA acceleration.The authors note this is common in evaluations of online methods.Source: §A.4 · p. 19
  • On FastParticle, the method trained for 100 iterations per frame versus Dynamic3DGS’s 300 had approximately twice the average training speed per iteration and outperformed Dynamic3DGS in efficiency and final performance.Source: §A.4 · p. 19
  • The authors report that SCas4D remains superior when comparing methods at equivalent wall time.Source: §A.4 · p. 19

A.5 Ablation Study on Number of Cluster Layers

On FastParticle, the layer-count ablation finds that three cluster layers offer the best efficiency–performance trade-off among the tested settings.

A.5 Ablation Study on Number of Cluster Layers

  • K = 3 performs best among the tested layer counts from 1 to 5 on FastParticle.Source: §A.5 · p. 19
  • Increasing the layer count beyond three brings no additional performance gains and adds parameters to optimize.The authors note that this may increase the required number of iterations.Source: §A.5 · p. 19
  • The authors conclude that K = 3 provides the best trade-off between efficiency and performance in their framework.Source: §A.5 · p. 19

A.6 Illustration of the Multi-Layer Structure

The appendix illustrates SCas4D’s layered structure and training, reports robustness and constraint ablations, compares deformation-based segmentation, and identifies remaining limitations.

A.6 Illustration of the Multi-Layer Structure

  • Figure 13 depicts coarse-to-fine multi-layer clustering for two FastParticle objects; colors denote clusters, and matching colors across layers do not imply correlation.Source: §A.6 · p. 19, Figure 13 · p. 20

A.7 Tracking Labels

  • The tracking ground truths are manually annotated 2D points selected for having distinct features that remain trackable across multiple frames.Figure 14 presents the annotated ground truths.Source: §A.7 · p. 20, Figure 14 · p. 21

A.8 Learning the Deformation

  • Training initializes Gaussians on the first static frame, clusters their centroids coarse-to-fine, then optimizes deformation functions by backpropagating losses for each time-frame input.The appendix also notes that learned deformation can be used to create motions or insert objects, raising a potential fake-news concern.Source: §A.8 · pp. 20, 22

A.9 Robustness to the number of clusters in the first layer

  • Performance remains comparable when the first-layer cluster count is reduced from the default N = 64, despite potentially coarser groupings and more clustering errors.The authors attribute robustness to the multi-layer refinement and finest-level per-Gaussian updates; Table 7 gives PSNR results across settings.Source: §A.9 · p. 22, Table 7 · p. 22

A.10 Effect of the Max Scale Constraint

  • Disabling the max scale constraint by setting max_scale very high significantly degrades PSNR; max_scale = 0.02 is the main-experiment threshold described as sufficient to maintain rendering quality.The appendix attributes the constraint’s role to preventing excessively large Gaussians that may introduce rendering artifacts.Source: §A.10 · p. 22, Table 8 · p. 22

A.11 Additional Experiment on Per-Gaussian Deformation for Segmentation

  • The multi-layer structure encourages coherent cluster motion and more stable, interpretable rotations, improving segmentation accuracy by reducing noise in estimated motion.The visual comparison contrasts per-Gaussian deformation with the multi-layer structure.Source: §A.11 · pp. 22–23, Figure 15 · p. 23

A.12 Limitations

  • Real-time training and rendering remain challenging despite reducing training to 100 iterations per frame; learned deformation is not fully used, and the articulated segmentation is not well refined.Source: §A.12 · p. 23
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