Hand-Held 3-D Reconstruction of Large-Scale Scene With Kinect Sensors Based on Surfel and Video Sequences

摘要

This letter presents a hand-held complex large-scale scene reconstruction method with Kinect sensors based on surfel and video sequences. The feature point method simultaneous localization and mapping (SLAM) is employed to estimate the pose of the camera, and then bundle adjustment by combining 2-D and 3-D feature points is used to optimize camera pose. Also, the surfel model is employed to construct deformation maps for the fusion and optimization of point clouds, and finally, an accurate precise 3-D map can be obtained. The main contribution of this letter is that: 1) by using the SLAM method to obtain camera pose as the initial value of optimization, the problem of insufficient memory and low efficiency of the structure form motion method can be well solved; 2) sparsely textured regions can be reconstructed better by using bundle adjustment by combining 2-D and 3-D feature points; and 3) dense 3-D reconstruction of large scenes can be achieved, and the reconstructed 3-D models are more elaborate. Finally, experimental results show that this proposed method can be applied to a variety of complex large-scale scenes, and can obtain accurate precise 3-D model. This presented 3-D reconstruction method can be widely used in the fields of human–computer interaction, consumer electronics, and virtual reality.