用动态变形场提升动态场景三维重建精度，兼顾细节与拓扑变化。
MoAngelo: Motion-Aware Neural Surface Reconstruction for Dynamic Scenes
- 基于初始帧模板，联合优化形变场跟踪动态变化。
- 在ActorsHQ数据集上优于现有最先进方法，几何更精细。
- 适合需要高保真动态3D重建的研究者与应用开发人员。
从多视角视频中重建动态场景是计算机视觉中的基础挑战。尽管最近的神经表面重建方法在静态三维重建上取得显著成果，但将其扩展到动态场景时面临巨大的计算和表征难题。现有动态方法侧重于新视角合成，导致提取的网格噪声较大；即使追求几何保真度的方法也常因问题病态性而生成过于平滑的网格。本文提出一种新框架，将静态三维重建方法NeuralAngelo拓展至动态场景。首先使用NeuralAngelo在初始帧上获取高质量模板场景，随后联合优化形变场以追踪该模板并根据时间序列进行精炼。这一灵活模板可更新几何以包含变形场无法建模的变化，如遮挡区域或拓扑变化。在ActorsHQ数据集上，本方法相比先前最先进方法表现出更优的重建精度。
原文摘要 · Abstract (English)
Dynamic scene reconstruction from multi-view videos remains a fundamental challenge in computer vision. While recent neural surface reconstruction methods have achieved remarkable results in static 3D reconstruction, extending these approaches with comparable quality for dynamic scenes introduces significant computational and representational challenges. Existing dynamic methods focus on novel-view synthesis, therefore, their extracted meshes tend to be noisy. Even approaches aiming for geometric fidelity often result in too smooth meshes due to the ill-posedness of the problem. We present a novel framework for highly detailed dynamic reconstruction that extends the static 3D reconstruction method NeuralAngelo to work in dynamic settings. To that end, we start with a high-quality template scene reconstruction from the initial frame using NeuralAngelo, and then jointly optimize deformation fields that track the template and refine it based on the temporal sequence. This flexible template allows updating the geometry to include changes that cannot be modeled with the deformation field, for instance occluded parts or the changes in the topology. We show superior reconstruction accuracy in comparison to previous state-of-the-art methods on the ActorsHQ dataset.
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