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Benjamin Ummenhofer
Benjamin Ummenhofer
Research Scientist, Intel Labs
Verified email at intel.com
Title
Cited by
Cited by
Year
Demon: Depth and motion network for learning monocular stereo
B Ummenhofer, H Zhou, J Uhrig, N Mayer, E Ilg, A Dosovitskiy, T Brox
Proceedings of the IEEE Conference on Computer Vision and Pattern†…, 2017
7802017
DeepTAM: Deep Tracking and Mapping
H Zhou, B Ummenhofer, T Brox
Proceedings of the European Conference on Computer Vision (ECCV), 822-838, 2018
2422018
Lagrangian fluid simulation with continuous convolutions
B Ummenhofer, L Prantl, N Thuerey, V Koltun
International Conference on Learning Representations, 2019
1732019
CAM-Convs: camera-aware multi-scale convolutions for single-view depth
JM Facil, B Ummenhofer, H Zhou, L Montesano, T Brox, J Civera
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern†…, 2019
1432019
Global, Dense Multiscale Reconstruction for a Billion Points
B Ummenhofer, T Brox
International Journal of Computer Vision, 2017
832017
Global, Dense Multiscale Reconstruction for a Billion Points
B Ummenhofer, T Brox
IEEE International Conference on Computer Vision (ICCV), 2015
832015
Point-based 3d reconstruction of thin objects
B Ummenhofer, T Brox
Proceedings of the IEEE International Conference on Computer Vision, 969-976, 2013
392013
DeepTAM: Deep tracking and mapping with convolutional neural networks
H Zhou, B Ummenhofer, T Brox
International Journal of Computer Vision 128 (3), 756-769, 2020
282020
Dense 3d reconstruction with a hand-held camera
B Ummenhofer, T Brox
Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium†…, 2012
242012
Temporally consistent depth estimation in videos with recurrent architectures
D Tananaev, H Zhou, B Ummenhofer, T Brox
Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0-0, 2018
232018
Adaptive Surface Reconstruction With Multiscale Convolutional Kernels
B Ummenhofer, V Koltun
Proceedings of the IEEE/CVF International Conference on Computer Vision†…, 2021
212021
Guaranteed conservation of momentum for learning particle-based fluid dynamics
L Prantl, B Ummenhofer, V Koltun, N Thuerey
Advances in Neural Information Processing Systems 35, 6901-6913, 2022
162022
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation
A Thyagharajan, B Ummenhofer, P Laddha, OJ Omer, S Subramoney
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern†…, 2022
92022
Introduction to Dense Reconstruction from Multiple Images
B Ummenhofer
Albert-Ludwigs-Universitšt Freiburg im Breisgau, 2018
12018
Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting
B Ummenhofer, S Agrawal, R Sepulveda, Y Lao, K Zhang, T Cheng, ...
arXiv preprint arXiv:2401.09126, 2024
2024
Applying self-confidence in multi-label classification to model training
A Thyagharajan, P Laddha, B Ummenhofer, OJ Omer
US Patent 11,875,555, 2024
2024
LEARNING NEURAL REFLECTANCE SHADERS FROM IMAGES
B Ummenhofer, S Wang, S Agrawal, Y Lao, K Zhang, S Richter, V Koltun
US Patent App. 17/849,055, 2023
2023
Segment fusion based robust semantic segmentation of scenes
A Thyagharajan, P Laddha, B Ummenhofer, OJ Omer
US Patent App. 17/582,390, 2022
2022
Multi-scale convolutional kernels for adaptive grids
B Ummenhofer, V Koltun
US Patent App. 17/528,829, 2022
2022
Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion
A Thyagharajan, B Ummenhofer, P Laddha, OJ Omer, S Subramoney
arXiv preprint arXiv:2111.08434, 2021
2021
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