24 citations · 71 across the 8 of their papers we have counts for
12 papers
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
Zhaoqi Leng, Shuyang Cheng, Benjamin Caine +5
Data augmentation is an important technique to improve data efficiency and save labeling cost for 3D detection in point clouds. Yet, existing augmentation policies have so far been…
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
Pei Sun, Mingxing Tan, Weiyue Wang +4
3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection comes from the inherent sparse na…
Multi-Class 3D Object Detection with Single-Class Supervision
Mao Ye, Chenxi Liu, Maoqing Yao +4
While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost. An alternative approach is to…
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation
Qiangeng Xu, Yin Zhou, Weiyue Wang +2
In autonomous driving, a LiDAR-based object detector should perform reliably at different geographic locations and under various weather conditions. While recent 3D detection resea…
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
Yuning Chai, Pei Sun, Jiquan Ngiam +5
3D object detection is vital for many robotics applications. For tasks where a 2D perspective range image exists, we propose to learn a 3D representation directly from this range i…
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Pei Sun, Weiyue Wang, Yuning Chai +5
The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by…