Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-51814-5_10 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
3D reconstruction,Color mapping,Registration,Frame-to-model matching,Optimization | Computer vision,Colored,Color mapping,Vertex (geometry),Pattern recognition,Computer science,Pose,Artificial intelligence,RGB color model,3D reconstruction,Global model | Conference |
Volume | ISSN | Citations |
10133 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 18 | 3 |