Depth estimation is one of the most important tasks in scene understanding. In the existing
joint self-supervised learning approaches of depth-pose estimation, depth estimation and …

There has been tremendous research progress in estimating the depth of a scene from a
monocular camera image. Existing methods for single-image depth prediction are …

Dense depth estimation from a single image is a key problem in computer vision, with
exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression …

Most existing methods often rely on complex models to predict scene depth with high
accuracy, resulting in slow inference that is not conducive to deployment. To better balance …

Abstract Monocular Depth Estimation (MDE) is a fundamental problem in computer vision
with numerous applications. Recently LIDAR-supervised methods have achieved …

Monocular depth estimation in the wild inherently predicts depth up to an unknown scale. To
resolve scale ambiguity issue, we present a learning algorithm that leverages monocular …

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 …

Self-supervised monocular depth and ego-motion estimation is a promising approach to
replace or supplement expensive depth sensors such as LiDAR for robotics applications like …

Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a
promising method as it refrains from the requirement of ground truth depth. Despite …

Self-supervised monocular depth estimation has been widely investigated to estimate depth
images and relative poses from RGB images. This framework is promising because the …