no code implementations • 12 Oct 2022 • Yuxuan Xue, Haolong Li, Stefan Leutenegger, Jörg Stückler
Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras.
no code implementations • 28 Sep 2020 • Arijit Mallick, Jörg Stückler, Hendrik Lensch
We use model-agnostic meta-learning (MAML) to train base parameters which, in turn, are adapted for multi-view stereo on new domains through self-supervised training.
no code implementations • 17 Sep 2020 • Rama Krishna Kandukuri, Jan Achterhold, Michael Möller, Jörg Stückler
Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images.
1 code implementation • L4DC 2020 • Nathanael Bosch, Jan Achterhold, Laura Leal-Taixé, Jörg Stückler
We propose to learn a deep latent Gaussian process dynamics (DLGPD) model that learns low-dimensional system dynamics from environment interactions with visual observations.
1 code implementation • 2 May 2020 • Francis Engelmann, Jörg Stückler, Bastian Leibe
In this paper, we propose to use 3D shape and motion priors to regularize the estimation of the trajectory and the shape of vehicles in sequences of stereo images.
1 code implementation • ICCV 2019 • Michael Strecke, Jörg Stückler
The majority of approaches for acquiring dense 3D environment maps with RGB-D cameras assumes static environments or rejects moving objects as outliers.
7 code implementations • 13 Apr 2019 • Vladyslav Usenko, Nikolaus Demmel, David Schubert, Jörg Stückler, Daniel Cremers
We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO.
no code implementations • 8 Aug 2018 • Hidenobu Matsuki, Lukas von Stumberg, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
We propose a novel real-time direct monocular visual odometry for omnidirectional cameras.
no code implementations • 6 Aug 2018 • Lingni Ma, Jörg Stückler, Tao Wu, Daniel Cremers
Dense pixelwise prediction such as semantic segmentation is an up-to-date challenge for deep convolutional neural networks (CNNs).
no code implementations • ECCV 2018 • David Schubert, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness.
no code implementations • ECCV 2018 • Nan Yang, Rui Wang, Jörg Stückler, Daniel Cremers
To this end, we incorporate deep depth predictions into Direct Sparse Odometry (DSO) as direct virtual stereo measurements.
4 code implementations • 17 Apr 2018 • David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements.
no code implementations • 26 Mar 2017 • Lingni Ma, Jörg Stückler, Christian Kerl, Daniel Cremers
At test time, the semantics predictions of our network can be fused more consistently in semantic keyframe maps than predictions of a network trained on individual views.
no code implementations • CVPR 2017 • Yevhen Kuznietsov, Jörg Stückler, Bastian Leibe
Supervised deep learning often suffers from the lack of sufficient training data.
no code implementations • 7 Feb 2017 • Anton Kasyanov, Francis Engelmann, Jörg Stückler, Bastian Leibe
Our visual-inertial SLAM system is based on a real-time capable visual-inertial odometry method that provides locally consistent trajectory and map estimates.
no code implementations • 26 Sep 2016 • Lukas von Stumberg, Vladyslav Usenko, Jakob Engel, Jörg Stückler, Daniel Cremers
Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity.