7 citations · 8 across the 4 of their papers we have counts for
6 papers · 1 filter
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving
Yinpeng Dong, Caixin Kang, Jinlai Zhang +6
3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to rea…
Adversarial samples for deep monocular 6D object pose estimation
Jinlai Zhang, Weiming Li, Shuang Liang +2
Estimating 6D object pose from an RGB image is important for many real-world applications such as autonomous driving and robotic grasping. Recent deep learning models have achieved…
Boosting 3D Adversarial Attacks with Attacking On Frequency
Binbin Liu, Jinlai Zhang, Lyujie Chen +1
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks. Recently, 3D adversarial attacks, especially adversarial attacks on point clouds, have elicited…
3D Adversarial Attacks Beyond Point Cloud
Jinlai Zhang, Lyujie Chen, Binbin Liu +5
Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks pe…
The art of defense: letting networks fool the attacker
Jinlai Zhang, Yinpeng Dong, Binbin Liu +5
Robust environment perception is critical for autonomous cars, and adversarial defenses are the most effective and widely studied ways to improve the robustness of environment perc…
PointCutMix: Regularization Strategy for Point Cloud Classification
Jinlai Zhang, Lyujie Chen, Bo Ouyang +5
As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent. In th…