Color-Introduced Frame-to-Model Registration for 3D Reconstruction

Fei Li, Yunfan Du, Rujie Liu

Abstract: 3D reconstruction has become an active research topic with the popularity of consumer-grade RGB-D cameras, and registration for model alignment is one of the most important steps. Most typical systems adopt depth-based geometry matching, while the captured color images are totally discarded. Some recent methods further introduce photometric cue for better results, but only frame-to-frame matching is used. In this paper, a novel registration approach is proposed. According to both geometric and photometric consistency, depth and color information are involved in a unified optimization framework. With the available depth maps and color images, a global model with colored surface vertices is maintained. The incoming RGB-D frames are aligned based on frame-to-model matching for more effective camera pose estimation. Both quantitative and qualitative experimental results demonstrate that better reconstruction performance can be obtained by our proposal.