We propose a method for adapting neural networks to distribution shifts at test-time. In
contrast to training-time robustness mechanisms that attempt to anticipate the shift, we …

Depth completion aims to predict a dense depth map from a sparse depth input. The
acquisition of dense ground truth annotations for depth completion settings can be difficult …

Depth completion is a widely studied problem of predicting a dense depth map from a
sparse set of measurements and a single RGB image. In this work, we approach this …

LiDAR depth completion is a task that predicts depth values for every pixel on the
corresponding camera frame, although only sparse LiDAR points are available. Most of the …

Depth completion is a vital task for autonomous driving as it involves reconstructing the
precise 3D geometry of a scene from sparse and noisy depth measurements. However most …

Depth completion aims to recover pixelwise depth from incomplete and noisy depth
measurements with or without the guidance of a reference RGB image. This task attracted …

It is challenging to apply depth maps generated from sparse laser scan data to computer
vision tasks, such as robot vision and autonomous driving, because of the sparsity and noise …

Self-supervised monocular depth prediction provides a cost-effective solution to obtain the
3D location of each pixel. However, the existing approaches usually lead to unsatisfactory …

In this work we investigate the problem of uncertainty estimation for image-guided depth
completion. We extend Deep Basis Fitting (DBF) for depth completion within a Bayesian …

The limitation of LiDAR (Light Detection And Ranging) sensor causes the general sparsity of
produced depth measurement. However, the sparse representation of the world is …