Planar object detection from 3D point clouds based on pyramid voxel representation

Planar detection is a fundamental task in many computer vision applications. This paper proposed a fast and reliable plane detection method from 3D point clouds to address the high computation cost problem in existing methods. The 3D space is first partitioned into pyramid voxels. Each 3D point is assigned to one voxel at each pyramid layer so that all the 3D points are represented by pyramid voxels. For each voxel, we apply the Eigen value decomposition to analyze 3D points inside and propose an index for fast plane detection. Especially, the plane index is efficiently computed with no explicit Eigen value decomposition to enhance the computation. The detected planar voxels are analyzed and merged for planar object detection based on geometric relationship between voxels. The proposed method uses voxel-wise instead of point-wise processing of the 3D point clouds so that it can greatly enhance the computation efficiency yet with good detection results. The proposed method has been validated with actual 3D point clouds collected by RGB-D sensor of Kinect 1.0 in both indoor and outdoor environments. The results demonstrate that the proposed method can quickly detect single and multiple planar objects in both environments. The precision and the accuracy of the proposed method are 97.1% and 94.5%, respectively. Compared to existing methods (e.g., Hough Transform, RANSAC), the proposed method can greatly enhance the computation efficiency in several orders of magnitudes.