Estimating depth from single RGB images and videos is of widespread interest due to its
applications in many areas, including autonomous driving, 3D reconstruction, digital …

Background: Maintaining a healthy diet is vital to avoid health-related issues, eg,
undernutrition, obesity and many non-communicable diseases. An indispensable part of the …

Self-supervised learning for depth estimation uses geometry in image sequences for
supervision and shows promising results. Like many computer vision tasks, depth network …

Multitask joint learning technology continues gaining more attention as a paradigm shift and
has shown promising performance in many applications. Depth estimation and semantic …

Most current visual SLAM systems rely on static environment assumptions. However, in
dynamic environments, the presence of dynamic objects can severely impair the …

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 …

Monocular depth inference is a fundamental problem for scene perception of robots. Specific
robots may be equipped with a camera plus an optional depth sensor of any type and …

Accurate real depth annotations are difficult to acquire, needing the use of special devices
such as a LiDAR sensor. Self-supervised methods try to overcome this problem by …

Six-dimensional (6D) pose estimation is an important branch in the field of robotics focused
on enhancing the ability of robots to manipulate and grasp objects. The latest research trend …

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 …