772 citations · 792 across the 5 of their papers we have counts for
5 papers
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics
Lukas Prantl, Benjamin Ummenhofer, Vladlen Koltun +1
We present a novel method for guaranteeing linear momentum in learned physics simulations. Unlike existing methods, we enforce conservation of momentum with a hard constraint, whic…
Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion
Anirud Thyagharajan, Benjamin Ummenhofer, Prashant Laddha +2
3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art seman…
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
Jose M. Facil, Benjamin Ummenhofer, Huizhong Zhou +3
Single-view depth estimation suffers from the problem that a network trained on images from one camera does not generalize to images taken with a different camera model. Thus, chan…
DeepTAM: Deep Tracking and Mapping
Huizhong Zhou, Benjamin Ummenhofer, Thomas Brox
We present a system for keyframe-based dense camera tracking and depth map estimation that is entirely learned. For tracking, we estimate small pose increments between the current…
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig +4
In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstraine…