用神经辐射场与图像分割结合，实现可变构型物体的结构估计与渲染。
NARF24: Estimating Articulated Object Structure for Implicit Rendering
- 基于多视角图像学习统一的神经辐射场，融合部件分割实现隐式空间定位。
- 从隐式定位中推断出物体的连接关系和关节参数，支持不同构型下的渲染。
- 适合机器人感知与复杂机械结构建模，尤其适用于少样本场景。
可动物体及其表征对机器人而言是难题，不仅需几何与纹理信息，还需连接关系与关节参数。本文提出一种方法，在少量采集场景上学习共享的神经辐射场（NeRF）表示，并结合基于部件的图像分割，实现隐式空间中的部件定位，由此估计出可动物体的连接方式与关节参数，从而支持配置条件下的渲染。
原文摘要 · Abstract (English)
Articulated objects and their representations pose a difficult problem for robots. These objects require not only representations of geometry and texture, but also of the various connections and joint parameters that make up each articulation. We propose a method that learns a common Neural Radiance Field (NeRF) representation across a small number of collected scenes. This representation is combined with a parts-based image segmentation to produce an implicit space part localization, from which the connectivity and joint parameters of the articulated object can be estimated, thus enabling configuration-conditioned rendering.
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