It is common to observe performance degradation when transferring models trained on
some (source) datasets to target testing data due to a domain gap between them. Existing …

Stereo-LiDAR fusion is a promising task in that we can utilize two different types of 3D
perceptions for practical usage-dense 3D information (stereo cameras) and highly-accurate …

Exploiting internal spatial geometric constraints of sparse LiDARs is beneficial to depth
completion, however, has been not explored well. This paper proposes an efficient method …

Depth completion aims to derive a dense depth map from sparse depth measurements with
a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly …

Light-weight time-of-flight (ToF) depth sensors are small, cheap, low-energy and have been
massively deployed on mobile devices for the purposes like autofocus, obstacle detection …

Abstract Semantic Scene Completion (SSC) transforms an image of single-view depth
and/or RGB 2D pixels into 3D voxels, each of whose semantic labels are predicted. SSC is a …

In many fields, self-supervised learning solutions are rapidly evolving and filling the gap with
supervised approaches. This fact occurs for depth estimation based on either monocular or …

Recovering a dense depth map from a pair of indoor RGB and sparse depth images in an
unsupervised manner is paramount in applications such as autonomous driving and 3D …

We present a method to infer a dense depth map from a color image and associated sparse
depth measurements. Our main contribution lies in the design of an annealing process for …

In this paper, we present a Structure-aware Cross-Modal Transformer (SCMT) to fully
capture the 3D structures hidden in sparse depths for depth completion. Most existing …