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CVPR2025顶会

Neural Inverse Rendering from Propagating Light

Anagh Malik, Benjamin Attal, Andrew Xie, Matthew O'Toole, David B. Lindell

2025年份
3顶会引用

摘要

estimated normals lidar frames (novel views) direct component indirect component time-resolved relighting (novel views) 2.62 ns 2.96 ns 2.37 ns 2.86 ns conventional image Figure 1. We introduce a method to model and invert multi-view, time-resolved measurements of propagating light from a flash lidar system. (row 1) Our method accurately recovers the geometry of this scene and enables rendering of time-resolved lidar measurements that reveal light propagation from novel views. (row 2) Physically-based modeling enables novel applications, such as time-resolved relighting and automatic decomposition of light transport into direct and indirect components.

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