形状跟踪:通过可微分路径跟踪从图像重建3D物体几何和SVBRDF材料

Purvi Goel, L. Cohen, James Guesman, V. Thamizharasan, J. Tompkin, Daniel Ritchie
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引用次数: 13

摘要

从多个视图重建物体几何和材料通常需要优化。可微路径跟踪是一个很有吸引力的框架,因为它可以再现复杂的外观效果。然而,由于计算成本高,难以使用。在本文中,我们探讨了如何使用可微光线追踪来细化初始粗网格和每网格面材料表示。在模拟中,我们发现可以从低分辨率的输入视图中重建精细的几何和材料细节,尽管路径跟踪的费用很高,但可以在几个小时内实现高质量的重建。重建成功地消除了材质属性中的阴影、阴影和全局照明效果(如漫反射)的歧义。我们演示了不同几何初始化的影响,包括空间雕刻,多视图立体和3D神经网络。最后,通过使用智能手机视频和消费者360°相机进行照明估计的输入,我们还展示了如何在不受约束的环境中改进真实世界物体的初始重建。
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Shape from Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing
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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