With the rapid development of high-speed communication and artificial intelligence
technologies, human perception of real-world scenes is no longer limited to the use of small …

Abstract 2D-based Industrial Anomaly Detection has been widely discussed, however,
multimodal industrial anomaly detection based on 3D point clouds and RGB images still has …

How important are training details and datasets to recent optical flow architectures like
RAFT? And do they generalize? To explore these questions, rather than develop a new …

Optical flow estimation is a fundamental problem of computer vision and has many
applications in the fields of robot learning and autonomous driving. This paper reveals novel …

Optical flow estimation is a fundamental task in the field of autonomous driving. Event
cameras are capable of responding to log-brightness changes in microseconds. Its …

As automated vehicles enter public roads, safety in a near-infinite number of driving
scenarios becomes one of the major concerns for the widespread adoption of fully …

Optical flow estimation is a basic task in self-driving and robotics systems, which enables to
temporally interpret traffic scenes. Autonomous vehicles clearly benefit from the ultra-wide …

Key-point-based scene understanding is fundamental for autonomous driving applications.
At the same time, optical flow plays an important role in many vision tasks. However, due to …

Anomaly detection methods have demonstrated remarkable success across various
applications. However, assessing their performance, particularly at the pixel-level, presents …

We present a shape-guided expert-learning framework to tackle the problem of
unsupervised 3D anomaly detection. Our method is established on the effectiveness of two …