Multi-View 3D Reconstruction of a Texture-Less Smooth Surface of Unknown Generic Reflectance

Ziang Cheng, Hongdong Li, Yuta Asano, Yinqiang Zheng, Imari Sato; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 16226-16235

Recovering the 3D geometry of a purely texture-less object with generally unknown surface reflectance (e.g. nonLambertian) is regarded as a challenging task in multiview reconstruction. The major obstacle revolves around establishing cross-view correspondences where photometric constancy is violated. This paper proposes a simple and practical solution to overcome this challenge based on a co-located camera-light scanner device. Unlike existing solutions, we do not explicitly solve for correspondence. Instead, we argue the problem is generally well-posed by multi-view geometrical and photometric constraints, and can be solved from a small number of input views. We formulate the reconstruction task as a joint energy minimization over the surface geometry and reflectance. Despite this energy is highly non-convex, we develop an optimization algorithm that robustly recovers globally optimal shape and reflectance even from a random initialization. Extensive experiments on both simulated and real data have validated our method, and possible future extensions are discussed