33 citations · 38 across the 4 of their papers we have counts for
11 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…
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…