MVR-3D : Reflectance-Based Multi-View 3D Reconstruction

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Reconstructing accurate 3D geometry from images remains a core challenge in computer vision, especially in the presence of non-Lambertian reflectance and complex lighting conditions. This paper addresses the limitations of traditional multi-view photometric stereo techniquesabada2022using,abada2022improved, which often rely on separate pipelines for geometry and reflectance, leading to unstable results in shadowed or specular regions. We propose a novel neural architecture that unifies photometric and geometric cues in a single optimization framework. Our method comprises four neural sub-networks that jointly model surface occupancy, color appearance, spatially-changing surface reflectance, and specular basis decomposition. Unlike previous approaches, we eliminate explicit normal prediction and instead optimize them implicitly through a physically-based rendering equation that accounts for shadow visibility and surface reflectance. Shadowed regions are dynamically excluded via an online ray-based masking strategy, enhancing robustness under varying illumination. The framework is built upon a volume-to-surface rendering mechanism. Experimental evaluations on synthetic and real-world datasets demonstrate significant improvements in surface detail, normal consistency, and material separation compared to state-of-the-art multi-view photometric stereo methods. Our code will be available on github after acceptance at: https://github.com/lyabada/MVR-3D.

Article activity feed