Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance
This addresses a challenging issue in computer vision for applications like robotics and inspection, though it appears incremental as it builds on existing multi-view and photometric approaches.
The paper tackled the problem of 3D reconstruction for texture-less objects with unknown non-Lambertian reflectance by proposing a method that uses multi-view constraints without explicit correspondence solving, achieving robust recovery of shape and reflectance from a small number of views.
Recovering the 3D geometry of a purely texture-less object with generally unknown surface reflectance (e.g. non-Lambertian) is regarded as a challenging task in multi-view 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.