LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of both academia and industry since it relies upon a sensor that incorporates appealing …
Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …
X Bai, Z Hu, X Zhu, Q Huang, Y Chen… - Proceedings of the …, 2022 - openaccess.thecvf.com
LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …
Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …
Non-uniformed 3D sparse data, eg, point clouds or voxels in different spatial positions, make contribution to the task of 3D object detection in different ways. Existing basic components in …
X Lai, Y Chen, F Lu, J Liu, J Jia - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from …
J Mao, Y Xue, M Niu, H Bai, J Feng… - Proceedings of the …, 2021 - openaccess.thecvf.com
Abstract We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds. Conventional 3D …
Lidar-based sensing drives current autonomous vehicles. Despite rapid progress, current Lidar sensors still lag two decades behind traditional color cameras in terms of resolution …
Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes …