Abstract：The 3D reconstruction system is mostly based on the simultaneous localization and mapping(SLAM) system of the feature point method and the direct method. The SLAM of feature point method cannot obtain good reconstruction results in the absence of feature points, while the SLAM of direct method has difficulty in estimating the pose with a fast-moving camera, and consequently, reconstruction results are unsatisfactory. To solve these problems, a dense 3D scene reconstruction system with a depth camera (RGB-D camera) based on semi-direct SLAM is proposed in this paper. The feature point method is exploited to estimate the camera pose in feature-rich areas. In the area of missing feature points, the direct method is utilized to estimate the pose of the camera. Then, the three-dimensional map is constructed by the optimized camera pose. The furfel model and the deformation map are utilized to estimate the pose of the point cloud and fuse point cloud. Finally, the ideal 3D reconstruction model is obtained. Experiments show that the system can be applied to all three-dimensional reconstruction of various occasions and acquire the ideal three-dimensional reconstruction model.
徐浩楠, 余雷, 费树岷. 基于半直接法SLAM的大场景稠密三维重建系统[J]. 模式识别与人工智能, 2018, 31(5): 477-484.
XU Haonan, YU Lei, FEI Shumin. Large Scene Dense 3D Reconstruction System Based on Semi-direct SLAM Method. , 2018, 31(5): 477-484.
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