3D Feature Matching for Point Cloud Object Extraction Effective object extraction plays an important role in many point cloudbased applications. This letter proposes a 3D feature matching framework for point cloud object extraction. To determine the optimal affine transformation parameters for each template feature point, a convex dissimilarity function and the locally affineinvariant geometric constraints are designed to construct the overall objective function. The 3D feature matching framework is integrated into a point cloud object extraction workflow. Extraction results on six test data sets show that average completeness, correctness, quality, and    F_1   measure of 0.96, 0.97, 0.93, and 0.96, respectively, are obtained in extracting light poles, vehicles, and palm trees. Comparative studies also confirm that the proposed method performs effectively and robustly, and exhibits superior or compatible performance over the other compared methods.

Research on 3D Object Pose Estimation Method in Intelligent Logistics In this paper, we propose a 3D object pose estimation method based on the frustum point cloud for industrial picking robot to grab the express. The core of our idea is to segment the point cloud of the object which should to be pick up first, then extract the best grab plane and grab point on the object point cloud. First of all, a mature 2D detection framework Faster RCNN is used to detect the object in RGB images. Then the 2D bounding box and category of the object closest to the camera will be output. On the basis of this, the frustum point cloud data of the detected object is generated by the combination of the depth map and the projection relationship of the RGBD camera. Then we used PointNet network to extract the feature of each point in the generated frustum point cloud. Finally, the instance segmentation of the point cloud and the subsequent extraction of the best grab plane and grab point are performed. We show results on both real dataset and synthetic dataset which we collected by ourselves, and our 3D instance segmentation network accuracy on real dataset reached 88%.