474 citations · 1.2k across the 17 of their papers we have counts for
17 papers
Learning to Adapt Multi-View Stereo by Self-Supervision
Arijit Mallick, Jörg Stückler, Hendrik Lensch
3D scene reconstruction from multiple views is an important classical problem in computer vision. Deep learning based approaches have recently demonstrated impressive reconstructio…
Learning to Identify Physical Parameters from Video Using Differentiable Physics
Rama Krishna Kandukuri, Jan Achterhold, Michael Möller +1
Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. V…
Planning from Images with Deep Latent Gaussian Process Dynamics
Nathanael Bosch, Jan Achterhold, Laura Leal-Taixé +1
Planning is a powerful approach to control problems with known environment dynamics. In unknown environments the agent needs to learn a model of the system dynamics to make plannin…
SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction
Francis Engelmann, Jörg Stückler, Bastian Leibe
Inferring the pose and shape of vehicles in 3D from a movable platform still remains a challenging task due to the projective sensing principle of cameras, difficult surface proper…
Where Does It End? -- Reasoning About Hidden Surfaces by Object Intersection Constraints
Michael Strecke, Joerg Stueckler
Dynamic scene understanding is an essential capability in robotics and VR/AR. In this paper we propose Co-Section, an optimization-based approach to 3D dynamic scene reconstruction…
EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association
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. The representation and trac…
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
Rui Wang, Nan Yang, Joerg Stueckler +1
Scene understanding from images is a challenging problem encountered in autonomous driving. On the object level, while 2D methods have gradually evolved from computing simple bound…
Visual-Inertial Mapping with Non-Linear Factor Recovery
Vladyslav Usenko, Nikolaus Demmel, David Schubert +2
Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems m…
Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras
Hidenobu Matsuki, Lukas von Stumberg, Vladyslav Usenko +2
We propose a novel real-time direct monocular visual odometry for omnidirectional cameras. Our method extends direct sparse odometry (DSO) by using the unified omnidirectional mode…
Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform
Lingni Ma, Jörg Stückler, Tao Wu +1
Dense pixelwise prediction such as semantic segmentation is an up-to-date challenge for deep convolutional neural networks (CNNs). Many state-of-the-art approaches either tackle th…
Direct Sparse Odometry with Rolling Shutter
David Schubert, Nikolaus Demmel, Vladyslav Usenko +2
Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness. In this paper, we propose a novel direct monocular VO method t…
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
Nan Yang, Rui Wang, Jörg Stückler +1
Monocular visual odometry approaches that purely rely on geometric cues are prone to scale drift and require sufficient motion parallax in successive frames for motion estimation a…
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
David Schubert, Thore Goll, Nikolaus Demmel +3
Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements trem…
Keyframe-Based Visual-Inertial Online SLAM with Relocalization
Anton Kasyanov, Francis Engelmann, Jörg Stückler +1
Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, w…
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras
Lingni Ma, Jörg Stückler, Christian Kerl +1
Visual scene understanding is an important capability that enables robots to purposefully act in their environment. In this paper, we propose a novel approach to object-class segme…
Semi-Supervised Deep Learning for Monocular Depth Map Prediction
Yevhen Kuznietsov, Jörg Stückler, Bastian Leibe
Supervised deep learning often suffers from the lack of sufficient training data. Specifically in the context of monocular depth map prediction, it is barely possible to determine…
From Monocular SLAM to Autonomous Drone Exploration
Lukas von Stumberg, Vladyslav Usenko, Jakob Engel +2
Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity. In order to implement autonomous navigation, algorithms are therefore desirable that use sens…