Frustum ConvNet: Sliding Frustums to Aggregate Local PointWise Features for Amodal In this work, we propose a novel method termed Frustum ConvNet (FConvNet) for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. FConvNet aggregates pointwise features as frustumlevel feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustumlevel features and supports an endtoend and continuous estimation of oriented boxes in the 3D space. We also propose component variants of FConvNet, including an FCN variant that extracts multiresolution frustum features, and a refined use of FConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. FConvNet assumes no prior knowledge of the working 3D environment and is thus datasetagnostic. We present experiments on both the indoor SUNRGBD and outdoor KITTI datasets. FConvNet outperforms all existing methods on SUNRGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. Code has been made available at: https:github.comzhixinwangfrustumconvnet.

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%.