12 citations · 42 across the 12 of their papers we have counts for
10 papers · 1 filter
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
Manuel Stoiber, Martin Sundermeyer, Rudolph Triebel
Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision. State-of-the-art approaches often rely on object texture to tackle this problem…
A Model for Multi-View Residual Covariances based on Perspective Deformation
Alejandro Fontan, Laura Oliva, Javier Civera +1
In this work, we derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. The core of our approach is the formulation of the residual…
Unknown Object Segmentation from Stereo Images
Maximilian Durner, Wout Boerdijk, Martin Sundermeyer +3
Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known…
"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences
Wout Boerdijk, Martin Sundermeyer, Maximilian Durner +1
We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. Our method successively learns an agnostic foreground segmentation followed…
DOT: Dynamic Object Tracking for Visual SLAM
Irene Ballester, Alejandro Fontan, Javier Civera +2
In this paper we present DOT (Dynamic Object Tracking), a front-end that added to existing SLAM systems can significantly improve their robustness and accuracy in highly dynamic en…
Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry
Kashmira Shinde, Jongseok Lee, Matthias Humt +2
This paper presents an end-to-end multi-modal learning approach for monocular Visual-Inertial Odometry (VIO), which is specifically designed to exploit sensor complementarity in th…