S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D Control Points

2024-08-23

arXiv.org

AI Summary

Dynamic 4D reconstruction of non-rigid scenes faces challenges with deformable Gaussian splatting, including slow convergence under complex motion and representation redundancy. To address this, we propose a motion-decoupled representation based on discrete 3D control points, explicitly separating ray propagation from 6-DoF local motion modeling—thereby overcoming the low-frequency bias inherent in implicit neural fields. Our method introduces a four-module decoupled pipeline: semantic segmentation, control point generation, object-level motion manipulation, and residual compensation—integrating differentiable Gaussian splatting with hybrid explicit-implicit rendering. We further design a multi-stage GPU-accelerated optimization scheme, achieving convergence in only ~100 iterations per frame (~2 seconds on an RTX 4070). Evaluated on Neu3DV and CMU-Panoptic benchmarks, our approach significantly outperforms state-of-the-art 4D Gaussian methods, enabling high-resolution, long-sequence, and robust real-time dynamic reconstruction.

Technology Category

• Computer Vision: Motion & Tracking
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Dynamic scene reconstruction using Gaussians has recently attracted increased interest. Mainstream approaches typically employ a global deformation field to warp a 3D scene in canonical space. However, the inherent low-frequency nature of implicit neural fields often leads to ineffective representations of complex motions. Moreover, their structural rigidity can hinder adaptation to scenes with varying resolutions and durations. To address these challenges, we introduce a novel approach for streaming 4D real-world reconstruction utilizing discrete 3D control points. This method physically models local rays and establishes a motion-decoupling coordinate system. By effectively merging traditional graphics with learnable pipelines, it provides a robust and efficient local 6-degrees-of-freedom (6-DoF) motion representation. Additionally, we have developed a generalized framework that integrates our control points with Gaussians. Starting from an initial 3D reconstruction, our workflow decomposes the streaming 4D reconstruction into four independent submodules: 3D segmentation, 3D control point generation, object-wise motion manipulation, and residual compensation. Experimental results demonstrate that our method outperforms existing state-of-the-art 4D Gaussian splatting techniques on both the Neu3DV and CMU-Panoptic datasets. Notably, the optimization of our 3D control points is achievable in 100 iterations and within just 2 seconds per frame on a single NVIDIA 4070 GPU.

Problem

• Addressing convergence challenges in dynamic 3D scene reconstruction
• Improving motion representation for deformable Gaussian splatting methods
• Separating observable and occluded motion components via hybrid optimization

Innovation

• Heterogeneous 3D control points combining optical flow and gradients
• Decouples observable motion via optical flow back-projection
• Refines occluded motion components through gradient-based optimization