With the advent of consumer grade depth sensors, low-cost volumetric capture systems are
easier to deploy. Their wider adoption though depends on their usability and by extension …

Precise localization using visual sensors is a fundamental requirement in many applications,
including robotics, augmented reality, and autonomous systems. Traditionally, the …

We propose deep virtual markers, a framework for estimating dense and accurate positional
information for various types of 3D data. We design a concept and construct a framework …

In this work, we tackle the challenging problem of category-level object pose and size
estimation from a single depth image. Although previous fully-supervised works have …

3D object-level mapping is a fundamental problem in robotics, which is especially
challenging when object CAD models are unavailable during inference. In this work, we …

While showing promising results, recent RGB-D camera-based category-level object pose
estimation methods have restricted applications due to the heavy reliance on depth sensors …

Navigating within dark and confined spaces require robotic platforms to utilize accurate and
reliable localization systems to operate safely and unattended. This paper presents an …

Introducing semantically meaningful objects to visual Simultaneous Localization And
Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose …

We propose-RANSAC, a generalized differentiable RANSAC that allows learning the entire
randomized robust estimation pipeline. The proposed approach enables the use of …

We present a system capable of reconstructing highly detailed object-level models and
estimating the 6D pose of objects by means of an RGB-D camera. In this work, we integrate …