Photometric Stereo using Gaussian Splatting and inverse rendering

TL;DR

This paper introduces a novel photometric stereo method that leverages Gaussian Splatting and inverse rendering, enabling more interpretable 3D scene reconstruction compared to neural network-based approaches.

Contribution

It presents a new approach combining Gaussian Splatting with inverse rendering for calibrated photometric stereo, moving away from neural network reliance.

Findings

Effective 3D scene parameterization using Gaussian Splatting

Simplified light model improves interpretability

Demonstrates potential for improved photometric stereo results

Abstract

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.