133 citations · 214 across the 6 of their papers we have counts for
7 papers
Point2Seq: Detecting 3D Objects as Sequences
Yujing Xue, Jiageng Mao, Minzhe Niu +5
We present a simple and effective framework, named Point2Seq, for 3D object detection from point clouds. In contrast to previous methods that normally {predict attributes of 3D obj…
Voxel Transformer for 3D Object Detection
Jiageng Mao, Yujing Xue, Minzhe Niu +5
We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds. Conventional 3D convolutional backbones in vo…
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
Jiageng Mao, Minzhe Niu, Haoyue Bai +3
We present a flexible and high-performance framework, named Pyramid R-CNN, for two-stage 3D object detection from point clouds. Current approaches generally rely on the points or v…
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving
Jianhua Han, Xiwen Liang, Hang Xu +8
Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale dataset for standardizing the evaluation of different self-super…
One Million Scenes for Autonomous Driving: ONCE Dataset
Jiageng Mao, Minzhe Niu, Chenhan Jiang +10
Current perception models in autonomous driving have become notorious for greatly relying on a mass of annotated data to cover unseen cases and address the long-tail problem. On th…
GRNet: Gridding Residual Network for Dense Point Cloud Completion
Haozhe Xie, Hongxun Yao, Shangchen Zhou +3
Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. Mainstream methods (e.g., PCN and TopNet) use Multi-layer P…