基于稀疏点云的多平面场景稠密重建

Dense Multi-planar Scene Reconstruction from Sparse Point Cloud

多平面场景是生活中常见的一种场景,然而由于该类场景中常常存在物体表面纹理缺乏和纹理重复的现象,导致从多视图像重建获得的三维点云数据中存在点云过于稀疏甚至孔洞等问题,进而导致以微面片拟合三维点云所得到的重建表面出现平面颠簸现象.针对这些问题,本文提出了一种基于稀疏点云的分段平面场景重建方法.首先,利用分层抽样代替随机抽样,改进了J-Linkage多模型估计算法;然后,利用该方法对稀疏点云进行多平面拟合,来获得场景的多平面模型;最后,将多平面模型和无监督的图像分割相结合,提取并重建场景中的平面区域.场景中的非平面部分用CMVS/PMVS(Clustering views for multi-view stereo/patch-based multi-view stereo)算法重建.多平面模型估计的实验表明,改进的J-Linkage算法提高了模型估计的准确度.三维重建的实验证实,提出的重建方法在有效地克服孔洞和平面颠簸问题的同时,还能重建出完整平面区域.

There are multi-planar scenes everywhere in our daily life. However, given its lack and self-repeat of the texture, there would be problems of over scarcity and holes on the reconstructed point cloud by the method of multi-view reconstruction. Further, there would be vacillation over the reconstructed facades using the method of fitting the reconstructed point cloud with miniature facets. To address these problems, we propose a method of piecewise reconstruction of each plane from the sparse point cloud. The proposed method first improves the J-linkage algorithm, with the stratified sampling instead of the random sampling. We then fit the point cloud with planes using the improved J-linkage algorithm, to obtain the multi-planar model of the scene. Finally, we extract and reconstruct the planar regions with the multi-planar model as well as an unsupervised segmentation algorithm. Besides, the non-planar areas are reconstructed by using the clustering views for multi-view stereo/patch-based multi-view stereo （CMVS/PMVS） algorithm. Experimental results of the multi-planar model demonstrate that the improved J-linkage algorithm can enhance the accuracy of the multi-planar model. Also, the experimental results of 3D reconstruction show that our method not only can effectively overcome holes and jaggies problems, but also can model the complete planar regions.