44 citations · 107 across the 17 of their papers we have counts for
8 papers · 1 filter
Accurate and Interactive Visual-Inertial Sensor Calibration with Next-Best-View and Next-Best-Trajectory Suggestion
Christopher L. Choi, Binbin Xu, Stefan Leutenegger
Visual-Inertial (VI) sensors are popular in robotics, self-driving vehicles, and augmented and virtual reality applications. In order to use them for any computer vision or state-e…
GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation & Tracking in the Wild
Simon Schaefer, Dorian F. Henning, Stefan Leutenegger
An accurate and uncertainty-aware 3D human body pose estimation is key to enabling truly safe but efficient human-robot interactions. Current uncertainty-aware methods in 3D human…
BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human Motion Tracking
Dorian F. Henning, Christopher Choi, Simon Schaefer +1
Robust, fast, and accurate human state - 6D pose and posture - estimation remains a challenging problem. For real-world applications, the ability to estimate the human state in rea…
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion
Xingxing Zuo, Nan Yang, Nathaniel Merrill +2
Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications. Feature volumes have recently b…
Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes
Binbin Xu, Andrew J. Davison, Stefan Leutenegger
In this paper, we propose a novel object-level mapping system that can simultaneously segment, track, and reconstruct objects in dynamic scenes. It can further predict and complete…
Towards the Probabilistic Fusion of Learned Priors into Standard Pipelines for 3D Reconstruction
Tristan Laidlow, Jan Czarnowski, Andrea Nicastro +2
The best way to combine the results of deep learning with standard 3D reconstruction pipelines remains an open problem. While systems that pass the output of traditional multi-view…