Nowadays, there are outstanding strides towards a future with autonomous vehicles on our
roads. While the perception of autonomous vehicles performs well under closed-set …

Detecting anomalous samples in set data is a problem attracting increased interest due to
novel modalities, such as point-cloud data produced by lidars. Novel methods including …

Complex sensors such as LiDAR, RADAR, and event cameras have proliferated in
autonomous robotics to enhance perception and understanding of the environment …

Anomaly detection, which is a critical and popular topic in computer vision, aims to detect
anomalous samples that are different from the normal (ie, non-anomalous) ones. The current …

Variational autoencoders (VAEs) play an important role in high-dimensional data generation
based on their ability to fuse the stochastic data representation with the power of recent …

Deep neural networks require specific layers to process point clouds, as the scattered and
irregular location of points prevents us from using convolutional filters. Here we introduce …

Deep generative networks provide a way to generalize complex multi-dimensional data
such as 3D point clouds. In this work, we present a novel method that operates on depth …

The rapid development of automated vehicles (AVs) promises to revolutionize transportation
by enhancing safety and efficiency. However, ensuring their reliability in diverse real-world …

In recent years there has been significant progress in the field of 3D learning on
classification, detection and segmentation problems. The vast majority of the existing studies …

Detecting anomalies within point clouds is crucial for various industrial applications, but
traditional unsupervised methods face challenges due to data acquisition costs, early-stage …