Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Generating robust and reliable correspondences across images is a fundamental task for a
diversity of applications. To capture context at both global and local granularity, we propose …

In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a
full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet 1 which has …

Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

Estimating 3D hand pose from single RGB images is a highly ambiguous problem that relies
on an unbiased training dataset. In this paper, we analyze cross-dataset generalization …

Abstract Neural Radiance Fields (NeRFs) have shown promise in applications like view
synthesis and depth estimation but learning from multiview images faces inherent …

Despite considerable progress on end-to-end optimized deep networks for image
compression, video coding remains a challenging task. Recently proposed methods for …

Full-motion cost volumes play a central role in current state-of-the-art optical flow methods.
However, constructed using simple feature correlations, they lack the ability to encapsulate …

Many real-world vision problems suffer from inherent ambiguities. In clinical applications for
example, it might not be clear from a CT scan alone which particular region is cancer tissue …