Robust 3D reconstruction with an RGB-D camera.

Abstract

We present a novel 3D reconstruction approach using a low-cost RGB-D camera such as Microsoft Kinect. Compared with previous methods, our scanning system can work well in challenging cases where there are large repeated textures and significant depth missing problems. For robust registration, we propose to utilize both visual and geometry features and combine SFM technique to enhance the robustness of feature matching and camera pose estimation. In addition, a novel prior-based multicandidates RANSAC is introduced to efficiently estimate the model parameters and significantly speed up the camera pose estimation under multiple correspondence candidates. Even when serious depth missing occurs, our method still can successfully register all frames together. Loop closure also can be robustly detected and handled to eliminate the drift problem. The missing geometry can be completed by combining multiview stereo and mesh deformation techniques. A variety of challenging examples demonstrate the effectiveness of the proposed approach.