Reconstructing object geometry and material from multiple views typically
requires optimization. Differentiable path tracing is an appealing framework as
it can reproduce complex appearance effects. However, it is difficult to use
due to high computational cost. In this paper, we explore how to use
differentiable ray tracing to refine an initial coarse mesh and per-mesh-facet
material representation. In simulation, we find that it is possible to
reconstruct fine geometric and material detail from low resolution input views,
allowing high-quality reconstructions in a few hours despite the expense of
path tracing. The reconstructions successfully disambiguate shading, shadow,
and global illumination effects such as diffuse interreflection from material
properties. We demonstrate the impact of different geometry initializations,
including space carving, multi-view stereo, and 3D neural networks. Finally,
with input captured using smartphone video and a consumer 360? camera for
lighting estimation, we also show how to refine initial reconstructions of
real-world objects in unconstrained environments.

通过不同 iable ray tracing 优化初始粗略网格和每个网格面材质表示，从而实现从低分辨率输入视图中重建细粒度几何和材料细节，本文证明不同 geometry 的初始化对重建的影响，并展示如何在无约束环境中通过智能手机视频和消费者 360° 摄像机的输入来优化实际物体的初始重建。