Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects

Yue Fan, Ivan Skorokhodov, Oleg Voynov, Savva Victorovich Ignatyev, Evgeny Burnaev, Peter Wonka, Yiqun Wang

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: Glossy surface, materials, illumination, diffuse, specular

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TL;DR: This paper presents a progressive inverse rendering method that efficiently recovers scene surfaces, materials, and lighting from multi-view images, effectively handling specular reflections.

Abstract: We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additional data and can handle glossy objects or bright lighting. It is a progressive inverse rendering approach, which consists of three stages. In the first stage, we reconstruct the scene radiance and signed distance function (SDF) with a novel regularization strategy for specular reflections. Our approach considers both the diffuse and specular colors, which allows for handling complex view-dependent lighting effects for surface reconstruction. In the second stage, we distill light visibility and indirect illumination from the learned SDF and radiance field using learnable mapping functions. Finally, we design a method for estimating the ratio of incoming direct light reflected in a specular manner and use it to reconstruct the materials and direct illumination. Experimental results demonstrate that the proposed method outperforms the current state-of-the-art in recovering surfaces, materials, and lighting without relying on any additional data.

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Submission Number: 2644