This paper is an overview to Visual Simultaneous Localization and Mapping (V-SLAM). We
discuss the basic definitions in the SLAM and vision system fields and provide a review of …

We present a novel self-distillation based self-supervised monocular depth estimation (SD-
SSMDE) learning framework. In the first step, our network is trained in a self-supervised …

Abstract 360deg cameras can capture complete environments in a single shot, which makes
360deg imagery alluring in many computer vision tasks. However, monocular depth …

Unsupervised methods have showed promising results on monocular depth estimation.
However, the training data must be captured in scenes without moving objects. To push the …

Recently, self-supervised learning technology has been applied to calculate depth and ego-
motion from monocular videos, achieving remarkable performance in autonomous driving …

Existing self-supervised monocular depth estimation methods can get rid of expensive
annotations and achieve promising results. However, these methods suffer from severe …

Predicting human motion from historical pose sequence is crucial for a machine to succeed
in intelligent interactions with humans. One aspect that has been obviated so far, is the fact …

Conventional self-supervised monocular depth prediction methods are based on a static
environment assumption, which leads to accuracy degradation in dynamic scenes due to the …

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise
photometric relation between temporally adjacent image frames. However, they neither fully …

Self-supervised monocular depth estimation enables robots to learn 3D perception from raw
video streams. This scalable approach leverages projective geometry and ego-motion to …