… With less than 4000 training point clouds, training our network from scratch will inevitably
suffer from overfitting. To reduce this issue, we introduce three different forms of data …

… detection methods. With such rich supervisions, we propose a novel partaware and aggregation
3D object detector, Part-A2 net, for 3D object detection from point cloud. Specifically, we …

… object detection. In this paper, we propose a novel Hierarchical Graph Network (HGNet) for
3D object detection on point clouds, … : a GConv based U-shape network (GU-net), a Proposal …

… 3D object detection in point clouds is a challenging problem due to discrete sampling, noisy
scans, occlusions and cluttered scenes, as illustrated in fig. 1. Many existing methods focus …

… network to detect objects from a LiDAR point cloud. Towards this end, we encode the point
cloud … network, named Point-GNN, to predict the category and shape of the object that each …

… of real-world point clouds for 3D object detection, our AGO-… features of real-world objects
and more complete features of … perceiving objects, and fundamentally exploits the network …

… our stage-1 sub-network directly generates a … object detection framework, named PointRCNN,
which directly operates on 3D point clouds and achieves robust and accurate 3D detection …

… far objects, simple voxelization did not help the network learn … -3Ddet for object detection in
point cloud, as depicted in … features, where 3D detection results are unsatisfactory due to the …

… -aware single-stage network for point cloud detection. Sec. 3.1 introduces our backbone
and detection networks. Sec.3.2 introduces the proposed auxiliary network to enrich the hidden …

… In this section, we introduce the SIENet for 3D object detection from point clouds. Section
3.1 introduces architecture details of our HP-RPN. Section 3.2 presents the SIE module, a …