most citedSWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds

2 citations · 2 across the 3 of their papers we have counts for

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cs.CV2022

LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations

Zhaoqi Leng, Guowang Li, Chenxi Liu +5

Data augmentations are important in training high-performance 3D object detectors for point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly…

cs.CV2022★ 2 cited

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…

cs.CV2022

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…