Spatiotemporal Bilateral Gradient Filtering for Inverse Rendering
DescriptionIn inverse rendering, gradient-based methods, which have seen great progress in the recent years, are typically used in conjunction with the Adam optimizer. While Adam usually improves convergence by temporally filtering gradients over previous iterations to reduce noise, it is not tailored to inverse rendering where the target signals (textures, volumes, or geometry) are usually piecewise smooth. Previous work has applied the inverse Laplacian operator to smooth gradients spatially, but this isotropic filtering can often lead to oversmoothing. We propose a spatiotemporal optimizer that can significantly speedup the convergence over Adam, by enforcing the optimization parameter updates to be piecewise smooth through a lightweight spatial domain cross-bilateral filter. We discuss different options of combining spatial filtering and Adam's temporal filtering, and provide intuitions for different scenarios. We show that our filtering leads to significantly higher-quality reconstructions in different inverse problems including texture, volume and geometry recovery.
Event Type
Technical Papers
TimeThursday, 5 December 20249:42am - 9:56am JST
LocationHall B5 (2), B Block, Level 5
