Authors
Yuan Liu, Peng Wang, Cheng Lin, Xiaoxiao Long, Jiepeng Wang, Lingjie Liu, Taku Komura, Wenping Wang
The University of Hong Kong; Tencent Games; University of Pennsylvania; Max Planck Institute for Informatics; Texas A&M University
Portals
Summary
We present NeRO for reconstructing the geometry and the BRDF of reflective objects with strong reflective appearances. NeRO only requires multiview input images of the reflective object under an unknown illumination condition. The output of NeRO is a triangular mesh with material parameters, which can easily be used in rendering software for relighting and other applications.
Abstract
We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment. Multiview reconstruction of reflective objects is extremely challenging because specular reflections are view-dependent and thus violate the multiview consistency, which is the cornerstone for most multiview reconstruction methods. Recent neural rendering techniques can model the interaction between environment lights and the object surfaces to fit the view-dependent reflections, thus making it possible to reconstruct reflective objects from multiview images. However, accurately modeling environment lights in the neural rendering is intractable, especially when the geometry is unknown. Most existing neural rendering methods, which can model environment lights, only consider direct lights and rely on object masks to reconstruct objects with weak specular reflections. Therefore, these methods fail to reconstruct reflective objects, especially when the object mask is not available and the object is illuminated by indirect lights. We propose a two-step approach to tackle this problem. First, by applying the split-sum approximation and the integrated directional encoding to approximate the shading effects of both direct and indirect lights, we are able to accurately reconstruct the geometry of reflective objects without any object masks. Then, with the object geometry fixed, we use more accurate sampling to recover the environment lights and the BRDF of the object. Extensive experiments demonstrate that our method is capable of accurately reconstructing the geometry and the BRDF of reflective objects from only posed RGB images without knowing the environment lights and the object masks.
Related Works
Multiview 3D reconstruction; Neural surface reconstruction; Reflective object reconstruction; BRDF estimation
Comparisons
NDR, NDRMC, NeuS, Ref-NeRF, MII: NeILF