Neural radiance fields (NeRFs) have recently emerged as a promising approach
for 3D reconstruction and novel view synthesis. However, NeRF-based methods
encode shape, reflectance, and illumination implicitly and this makes it
challenging for users to manipulate these properties in the rendered images
explicitly. Existing approaches only enable limited editing of the scene and
deformation of the geometry. Furthermore, no existing work enables accurate
scene illumination after object deformation. In this work, we introduce SPIDR,
a new hybrid neural SDF representation. SPIDR combines point cloud and neural
implicit representations to enable the reconstruction of higher quality object
surfaces for geometry deformation and lighting estimation. meshes and surfaces
for object deformation and lighting estimation. To more accurately capture
environment illumination for scene relighting, we propose a novel neural
implicit model to learn environment light. To enable more accurate illumination
updates after deformation, we use the shadow mapping technique to approximate
the light visibility updates caused by geometry editing. We demonstrate the
effectiveness of SPIDR in enabling high quality geometry editing with more
accurate updates to the illumination of the scene.

本文提出了一种新型的混合神经 SDF 表示方法 SPIDR，专门用于解决 NeRF 方法在渲染图像中难以显示物体形状、反射和光照等属性的问题，并结合阴影映射技术和神经隐式模型来更精准地进行几何编辑和场景照明更新。