Photometric Stereo using Gaussian Splatting and inverse rendering
This work addresses 3D reconstruction in computer vision, but it appears incremental as it adapts existing techniques to a specific domain.
The paper tackled photometric stereo by applying Gaussian Splatting and inverse rendering to parameterize and optimize 3D scenes, resulting in a more interpretable method that demonstrates potential for this problem.
Recent state-of-the-art algorithms in photometric stereo rely on neural networks and operate either through prior learning or inverse rendering optimization. Here, we revisit the problem of calibrated photometric stereo by leveraging recent advances in 3D inverse rendering using the Gaussian Splatting formalism. This allows us to parameterize the 3D scene to be reconstructed and optimize it in a more interpretable manner. Our approach incorporates a simplified model for light representation and demonstrates the potential of the Gaussian Splatting rendering engine for the photometric stereo problem.