most citedTransformation-Equivariant 3D Object Detection for Autonomous Driving

13 citations · 20 across the 4 of their papers we have counts for

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cs.CV20231 cited

DSMNet: Deep High-precision 3D Surface Modeling from Sparse Point Cloud Frames

Changjie Qiu, Zhiyong Wang, Xiuhong Lin +3

Existing point cloud modeling datasets primarily express the modeling precision by pose or trajectory precision rather than the point cloud modeling effect itself. Under this deman…

cs.CV20232 cited

CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions

Ming Yan, Xin Wang, Yudi Dai +5

Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dan…

cs.CV20231 cited

Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network

Weiquan Liu, Minghao Liu, Shijun Zheng +1

Although 3D point cloud classification neural network models have been widely used, the in-depth interpretation of the activation of the neurons and layers is still a challenge. We…

cs.CV2023

Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality

Weiquan Liu, Shijun Zheng, Cheng Wang

As the key technology of augmented reality (AR), 3D recognition and tracking are always vulnerable to adversarial examples, which will cause serious security risks to AR systems. A…

cs.CV202311 cited

Virtual Sparse Convolution for Multimodal 3D Object Detection

Hai Wu, Chenglu Wen, Shaoshuai Shi +2

Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points gen…

cs.CV202213 cited

Transformation-Equivariant 3D Object Detection for Autonomous Driving

Hai Wu, Chenglu Wen, Wei Li +3

3D object detection received increasing attention in autonomous driving recently. Objects in 3D scenes are distributed with diverse orientations. Ordinary detectors do not explicit…