论文标题

从跟踪:从图像通过可区分路径跟踪重建3D对象几何和SVBRDF材料的形状

Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing

论文作者

Goel, Purvi, Cohen, Loudon, Guesman, James, Thamizharasan, Vikas, Tompkin, James, Ritchie, Daniel

论文摘要

从多个视图中重建对象几何和材料通常需要优化。可区分的路径追踪是一个吸引人的框架,因为它可以再现复杂的外观效果。但是,由于高计算成本,很难使用。在本文中,我们探讨了如何使用可区分的射线跟踪来完善初始的粗网格和每分半面积的材料表示。在模拟中,我们发现可以从低分辨率输入视图中重建细微的几何和材料细节,尽管耗费了路径跟踪,但仍可以在几个小时内进行高质量的重建。这些重建成功地消除了阴影,阴影和全球照明效应,例如从材料特性中弥漫性互惠。我们展示了不同几何初始化的影响,包括太空雕刻,多视图立体声和3D神经网络。最后,使用智能手机视频和消费者360捕获输入?用于照明估算的相机,我们还展示了如何在不受约束的环境中完善对现实对象的初始重建。

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.

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