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 …

3D object classification using deep neural networks has been extremely successful. As the
problem of identifying 3D objects has many safety-critical applications, the neural networks …

In this paper, we look into the problem of 3D adversary attack, and propose to leverage the
internal properties of the point clouds and the adversarial examples to design a new self …

The importance of training robust neural network grows as 3D data is increasingly utilized in
deep learning for vision tasks in robotics, drone control, and autonomous driving. One …

Registration methods for point clouds have become a key component of many SLAM
systems on autonomous vehicles. However, an accurate estimate of the uncertainty of such …

Classification of 3D point clouds is a challenging machine learning (ML) task with important
real-world applications in a spectrum from autonomous driving and robot-assisted surgery to …

Critical robotic systems are systems whose functioning is critical to both ensuring the
accomplishment of a given mission and preventing the endangerment of life and the …

Classification of 3D point clouds is a challenging machine learning (ML) task with important
real-world applications in a spectrum from autonomous driving and robot-assisted surgery to …

Abstract 3D point cloud semantic analysis is challenging due to irregular locations and ill-
posed sparse representations. In this study, we explore the intrinsic structures of point …