Recently, the RGB images and point clouds fusion methods have been proposed to jointly
estimate 2D optical flow and 3D scene flow. However, as both conventional RGB cameras …

This paper proposes a novel framework integrating the principles of active stereo in
standard passive camera systems without a physical pattern projector. We virtually project a …

Depth completion is the task of generating a dense depth map given an image and a sparse
depth map as inputs. In this paper, we present OGNI-DC, a novel framework for depth …

This paper proposes a new framework for depth completion robust against domain-shifting
issues. It exploits the generalization capability of modern stereo networks to face depth …

High frame rate and accurate depth estimation plays an important role in several tasks
crucial to robotics and automotive perception. To date, this can be achieved through ToF …

Depth completion (DC) aims to predict a dense depth map from an RGB image and sparse
depth observations. Existing methods for DC generalize poorly on new datasets or unseen …

Depth completion upgrades sparse depth measurements into dense depth maps guided by
a conventional image. Existing methods for this highly ill-posed task operate in tightly …

Self-supervised multi-frame depth estimation methods only require unlabeled monocular
videos for training. However, most existing methods face challenges, including accuracy …

LiDAR semantic segmentation is receiving increased attention due to its deployment in
autonomous driving applications. As LiDARs come often with other sensors such as RGB …

Depth completion, predicting dense depth maps from sparse depth measurements, is an ill-
posed problem requiring prior knowledge. Recent methods adopt learning-based …