动态场景重建新方法，区分动静元素并分层建模运动。
DynaSplat: Dynamic-Static Gaussian Splatting with Hierarchical Motion Decomposition for Scene Reconstruction
- 通过形变统计与2D运动一致性融合，分离静态与动态元素。
- 分层建模全局与局部运动，精准捕捉非刚性变形。
- 基于物理的不透明度估计，提升遮挡下的重建质量。
重建复杂多变的环境仍是计算机视觉的核心挑战，现有方法在真实世界动态场景前常失效。本文提出DynaSplat，通过引入动态-静态分离与分层运动建模，将高斯点阵扩展至动态场景。首先，利用形变偏移统计与2D运动流一致性融合，实现场景元素的静动分类，优化空间表征，聚焦于关键运动区域。其次，提出分层运动建模策略，同时捕捉粗粒度全局变换与细粒度局部运动，实现对复杂非刚性运动的精确建模。最后，集成基于物理的不透明度估计，确保在复杂遮挡与视角变化下仍能生成视觉连贯的重建结果。在多个挑战性数据集上的大量实验表明，DynaSplat不仅在精度与真实感上超越现有最先进方法，且提供了更直观、紧凑、高效的动态场景重建路径。
原文摘要 · Abstract (English)
Reconstructing intricate, ever-changing environments remains a central ambition in computer vision, yet existing solutions often crumble before the complexity of real-world dynamics. We present DynaSplat, an approach that extends Gaussian Splatting to dynamic scenes by integrating dynamic-static separation and hierarchical motion modeling. First, we classify scene elements as static or dynamic through a novel fusion of deformation offset statistics and 2D motion flow consistency, refining our spatial representation to focus precisely where motion matters. We then introduce a hierarchical motion modeling strategy that captures both coarse global transformations and fine-grained local movements, enabling accurate handling of intricate, non-rigid motions. Finally, we integrate physically-based opacity estimation to ensure visually coherent reconstructions, even under challenging occlusions and perspective shifts. Extensive experiments on challenging datasets reveal that DynaSplat not only surpasses state-of-the-art alternatives in accuracy and realism but also provides a more intuitive, compact, and efficient route to dynamic scene reconstruction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。