Recent work has shown that CNN-based depth and ego-motion estimators can be learned
using unlabelled monocular videos. However, the performance is limited by unidentified …

While convolutional neural networks have shown a tremendous impact on various computer
vision tasks, they generally demonstrate limitations in explicitly modeling long-range …

Autonomous systems possess the features of inferring their own state, understanding their
surroundings, and performing autonomous navigation. With the applications of learning …

Self-supervised depth estimation for indoor environments is more challenging than its
outdoor counterpart in at least the following two aspects:(i) the depth range of indoor …

Can we relocalize in a scene represented by a single reference image? Standard visual
relocalization requires hundreds of images and scale calibration to build a scene-specific …

Localization in urban environments is becoming increasingly important and used in tools
such as ARCore, ARKit and others. One popular mechanism to achieve accurate indoor …

Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-
frame pose estimation. In this paper, we present a self-supervised learning method for VO …

We present a novel framework named PlaneMVS for 3D plane reconstruction from multiple
input views with known camera poses. Most previous learning-based plane reconstruction …

Geometric constraints are shown to enforce scale consistency and remedy the scale
ambiguity issue in self-supervised monocular depth estimation. Meanwhile, scale-invariant …

Monocular Depth Estimation (MDE) is a critical component in applications such as
autonomous driving. There are various attacks against MDE networks. These attacks …