Virtual reality, augmented reality, robotics, and autonomous driving, have recently attracted
much attention from both academic and industrial communities, in which image-based …

We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical
flow and ego-motion estimation from videos. The three components are coupled by the …

Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a
comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate …

We propose a single-shot approach for simultaneously detecting an object in an RGB image
and predicting its 6D pose without requiring multiple stages or having to examine multiple …

Numerous deep learning applications benefit from multi-task learning with multiple
regression and classification objectives. In this paper we make the observation that the …

In this paper, we propose a new unsupervised domain adaptation approach called
Collaborative and Adversarial Network (CAN) through domain-collaborative and domain …

Visual localization enables autonomous vehicles to navigate in their surroundings and
augmented reality applications to link virtual to real worlds. Practical visual localization …

We propose a novel monocular visual odometry (VO) system called UnDeepVO in this
paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth …

Maps are a key component in image-based camera localization and visual SLAM systems:
they are used to establish geometric constraints between images, correct drift in relative …

Popular research areas like autonomous driving and augmented reality have renewed the
interest in image-based camera localization. In this work, we address the task of predicting …