Near-Light Color Photometric Stereo for Mono-Chromatic Non-Lambertian Surfaces

TL;DR

This paper introduces a neural implicit framework for single-shot surface reconstruction of non-Lambertian, near-light surfaces using color photometric stereo, validated with a novel optical tactile sensor.

Contribution

It presents a new neural implicit method for near-light, non-Lambertian surface reconstruction from a single image, addressing practical lighting and material conditions.

Findings

Accurate surface reconstruction on synthetic datasets

Robust performance on real-world data

Effective validation with a custom optical tactile sensor

Abstract

Color photometric stereo enables single-shot surface reconstruction, extending conventional photometric stereo that requires multiple images of a static scene under varying illumination to dynamic scenarios. However, most existing approaches assume ideal distant lighting and Lambertian reflectance, leaving more practical near-light conditions and non-Lambertian surfaces underexplored. To overcome this limitation, we propose a framework that leverages neural implicit representations for depth and BRDF modeling under the assumption of mono-chromaticity (uniform chromaticity and homogeneous material), which alleviates the inherent ill-posedness of color photometric stereo and allows for detailed surface recovery from just one image. Furthermore, we design a compact optical tactile sensor to validate our approach. Experiments on both synthetic and real-world datasets demonstrate that our method achieves accurate and robust surface reconstruction.