Perception plays a pivotal role in autonomous driving systems, which utilizes onboard
sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings …

Geometric deep learning is increasingly important thanks to the popularity of 3D sensors.
Inspired by the recent advances in NLP domain, the self-attention transformer is introduced …

Modern autonomous driving systems rely heavily on deep learning models to process point
cloud sensory data; meanwhile, deep models have been shown to be susceptible to …

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 this …

As 3D object detection on point clouds relies on the geometrical relationships between the
points, non-standard object shapes can hinder a method's detection capability. However, in …

Recently, several attempts have demonstrated that 3D deep learning models are as
vulnerable to adversarial example attacks as 2D models. However, these methods are still …

Neural networks are vulnerable to adversarial examples, which poses a threat to their
application in security sensitive systems. We propose a Denoiser and UPsampler Network …

Abstract 3D point cloud classification has many safety-critical applications such as
autonomous driving and robotic grasping. However, several studies showed that it is …

Previous work has shown that Deep Neural Networks (DNNs), including those currently in
use in many fields, are extremely vulnerable to maliciously crafted inputs, known as …

Deep neural networks have made tremendous progress in 3D point-cloud recognition.
Recent works have shown that these 3D recognition networks are also vulnerable to …