Estimating the accurate depth from a single image is challenging since it is inherently
ambiguous and ill-posed. While recent works design increasingly complicated and powerful …

A growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their
promise for enhanced safety, efficiency, and economic benefits. While previous surveys …

Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene
information from single camera images, which is trainable on arbitrary image sequences …

Self-supervised learning is showing great promise for monocular depth estimation, using
geometry as the only source of supervision. Depth networks are indeed capable of learning …

A lot of research has been conducted in recent years on stereo depth estimation techniques,
taking the traditional approach to a new level such that it is in an appreciably good form for …

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

Estimating scene geometry from cost-effective sensors is key for robots. In this paper, we
study the problem of predicting dense depth from a single RGB image (monodepth) with …

Semantic segmentation is used in many fields, and most fields not only require models with
high-quality predictions but also require real-time speed in the forward inference phase …

State-of-the-art self-supervised learning approaches for monocular depth estimation usually
suffer from scale ambiguity. They do not generalize well when applied on distance …

Recent studies show a significant growth in semantic segmentation. However, many
semantic segmentation models still have a large number of parameters, making them …