{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/voxelnet-end-to-end-learning-for-point-cloud","title":"VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection","arxiv_id":"1711.06396","date":"2017-11-17","proceeding":"CVPR 2018 6","authors":["Yin Zhou","Oncel Tuzel"],"abstract":"Accurate detection of objects in 3D point clouds is a central problem in many\napplications, such as autonomous navigation, housekeeping robots, and\naugmented/virtual reality. To interface a highly sparse LiDAR point cloud with\na region proposal network (RPN), most existing efforts have focused on\nhand-crafted feature representations, for example, a bird's eye view\nprojection. In this work, we remove the need of manual feature engineering for\n3D point clouds and propose VoxelNet, a generic 3D detection network that\nunifies feature extraction and bounding box prediction into a single stage,\nend-to-end trainable deep network. Specifically, VoxelNet divides a point cloud\ninto equally spaced 3D voxels and transforms a group of points within each\nvoxel into a unified feature representation through the newly introduced voxel\nfeature encoding (VFE) layer. In this way, the point cloud is encoded as a\ndescriptive volumetric representation, which is then connected to a RPN to\ngenerate detections. Experiments on the KITTI car detection benchmark show that\nVoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a\nlarge margin. Furthermore, our network learns an effective discriminative\nrepresentation of objects with various geometries, leading to encouraging\nresults in 3D detection of pedestrians and cyclists, based on only LiDAR.","url_abs":"http://arxiv.org/abs/1711.06396v1","url_pdf":"http://arxiv.org/pdf/1711.06396v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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