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ORCIDRené Schuster
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2020 – today
- 2024
[j2]Shishir Muralidhara
, Sravan Kumar Jagadeesh, René Schuster, Didier Stricker:
JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation. SN Comput. Sci. 5(1): 187 (2024)- [c37]Michael Fürst, Rahul Jakkamsetty, René Schuster, Didier Stricker:
Learned Fusion: 3D Object Detection Using Calibration-Free Transformer Feature Fusion. ICPRAM 2024: 215-223 - [c36]Katharina Bendig, René Schuster, Didier Stricker:
ShapeAug: Occlusion Augmentation for Event Camera Data. ICPRAM 2024: 352-359 - [i29]Katharina Bendig, René Schuster, Didier Stricker:
ShapeAug: Occlusion Augmentation for Event Camera Data. CoRR abs/2401.02274 (2024) - [i28]Ramy Battrawy, René Schuster, Didier Stricker:
RMS-FlowNet++: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds. CoRR abs/2407.01129 (2024) - [i27]Ramy Battrawy, René Schuster, Didier Stricker:
EgoFlowNet: Non-Rigid Scene Flow from Point Clouds with Ego-Motion Support. CoRR abs/2407.02920 (2024) - [i26]Shishir Muralidhara, Saqib Bukhari, Georg Schneider, Didier Stricker, René Schuster:
CLEO: Continual Learning of Evolving Ontologies. CoRR abs/2407.08411 (2024) - 2023
- [c35]Ramy Battrawy, René Schuster, Didier Stricker:
EgoFlowNet: Non-Rigid Scene Flow from Point Clouds with Ego-Motion Support. BMVC 2023: 441-443 - [c34]Sravan Kumar Jagadeesh, René Schuster, Didier Stricker:
Multi-task Fusion for Efficient Panoptic-Part Segmentation. ICPRAM 2023: 15-26 - [c33]Christian Witte, René Schuster, Syed Saqib Bukhari, Patrick Trampert, Didier Stricker, Georg Schneider:
Severity of Catastrophic Forgetting in Object Detection for Autonomous Driving. ICPRAM 2023: 262-269 - [c32]Katharina Bendig, René Schuster, Didier Stricker:
On the Future of Training Spiking Neural Networks. ICPRAM 2023: 466-473 - [c31]Ankit Sonthalia, Ramy Battrawy, René Schuster, Didier Stricker:
EvLiDAR-Flow: Attention-Guided Fusion Between Point Clouds and Events for Scene Flow Estimation. ICPRAM 2023: 733-742 - [c30]Dipam Goswami, René Schuster, Joost van de Weijer, Didier Stricker:
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation. WACV 2023: 3194-3203 - [i25]Shishir Muralidhara, Sravan Kumar Jagadeesh, René Schuster, Didier Stricker:
JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation. CoRR abs/2311.18618 (2023) - [i24]Michael Fürst, Rahul Jakkamsetty, René Schuster, Didier Stricker:
Learned Fusion: 3D Object Detection using Calibration-Free Transformer Feature Fusion. CoRR abs/2312.09082 (2023) - 2022
- [b1]René Schuster:
Data-driven and Sparse-to-Dense Concepts in Scene Flow Estimation for Automotive Applications. Kaiserslautern University of Technology, Germany, Dr. Hut 2022, ISBN 978-3-8439-5039-8, pp. 1-227 - [c29]Marco Nemetz, Sandra Pfiel, Reinhard Altenburger, Florian Tiefenbacher, Matej Hopp, René Schuster, Michael Reiner:
Train@Train - A Case Study of Using Immersive Learning Environments for Health and Safety Training for the Austrian Railway Company. EuroSPI 2022: 781-789 - [c28]Michael Reiner, Marco Nemetz, Sandra Pfiel, Florian Tiefenbacher, Matej Hopp, René Schuster:
NOEDIKOM - The Digitization of Cultural Heritage/gems in Municipalities in Lower Austrian. EuroSPI 2022: 790-797 - [c27]Marco Nemetz, Sandra Pfiel, Reinhard Altenburger, Florian Tiefenbacher, Matej Hopp, René Schuster, Michael Reiner:
VRWalk - a Case Study Regarding Different Movement Options in Virtual Reality. EuroSPI 2022: 811-821 - [c26]Katharina Bendig, René Schuster, Didier Stricker:
Self-Superflow: Self-Supervised Scene Flow Prediction in Stereo Sequences. ICIP 2022: 481-485 - [c25]Michael Fürst, Priyash Bhugra, René Schuster, Didier Stricker:
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time. ICPR 2022: 3457-3463 - [c24]Ramy Battrawy, René Schuster, Mohammad-Ali Nikouei Mahani, Didier Stricker:
RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds. ICRA 2022: 883-889 - [i23]Ramy Battrawy, René Schuster, Mohammad-Ali Nikouei Mahani, Didier Stricker:
RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds. CoRR abs/2204.00354 (2022) - [i22]Katharina Bendig, René Schuster, Didier Stricker:
Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences. CoRR abs/2206.15296 (2022) - [i21]Dipam Goswami, René Schuster, Joost van de Weijer, Didier Stricker:
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation. CoRR abs/2210.07207 (2022) - [i20]Michael Fürst, Priyash Bhugra, René Schuster, Didier Stricker:
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time. CoRR abs/2211.15505 (2022) - [i19]Sravan Kumar Jagadeesh, René Schuster, Didier Stricker:
Multi-task Fusion for Efficient Panoptic-Part Segmentation. CoRR abs/2212.07671 (2022) - 2021
- [c23]Syed Muhammad Kumail Raza, René Schuster, Didier Stricker:
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching. CSCS 2021: 1:1-1:10 - [c22]Sandra Pfiel, Helena Lovasz-Bukvova, Florian Tiefenbacher, Matej Hopp, René Schuster, Michael Reiner, Deepak Dhungana:
Virtual Reality Applications for Experiential Tourism - Curator Application for Museum Visitors. EuroSPI 2021: 719-729 - [c21]René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker:
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation. WACV 2021: 197-206 - [c20]René Schuster, Christian Unger, Didier Stricker:
A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions. WACV 2021: 247-255 - [i18]Kumail Raza, René Schuster, Didier Stricker:
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching. CoRR abs/2110.12769 (2021) - 2020
- [j1]René Schuster, Oliver Wasenmüller, Christian Unger, Georg Kuschk, Didier Stricker:
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation. Int. J. Comput. Vis. 128(2): 527-546 (2020) - [c19]René Schuster, Didier Stricker, Christian Unger:
MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow. CSCS 2020: 1:1-1:8 - [c18]Natalie Horvath, Sandra Pfiel, Florian Tiefenbacher, René Schuster, Michael Reiner:
Analysis of Improvement Potentials in Current Virtual Reality Applications by Using Different Ways of Locomotion. EuroSPI 2020: 807-819 - [c17]Rishav, René Schuster, Ramy Battrawy, Oliver Wasenmüller, Didier Stricker:
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching. ICPR 2020: 180-187 - [c16]Michael Fürst, Shriya T. P. Gupta, René Schuster, Oliver Wasenmüller, Didier Stricker:
HPERL: 3D Human Pose Estimation from RGB and LiDAR. ICPR 2020: 7321-7327 - [c15]Rishav, Ramy Battrawy, René Schuster, Oliver Wasenmüller, Didier Stricker:
DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR. IROS 2020: 10460-10467 - [c14]Ramy Battrawy, René Schuster, Oliver Wasenmüller, Qing Rao, Didier Stricker:
deepRGBXYZ: Dense Pixel Description Utilizing RGB and Depth with Stacked Dilated Convolutions. ITSC 2020: 1-8 - [i17]Rishav, René Schuster, Ramy Battrawy, Oliver Wasenmüller, Didier Stricker:
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching. CoRR abs/2006.12235 (2020) - [i16]Rishav, Ramy Battrawy, René Schuster, Oliver Wasenmüller, Didier Stricker:
DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR. CoRR abs/2008.08136 (2020) - [i15]René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker:
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation. CoRR abs/2008.09346 (2020) - [i14]Michael Fürst, Shriya T. P. Gupta, René Schuster, Oliver Wasenmüller, Didier Stricker:
HPERL: 3D Human Pose Estimation from RGB and LiDAR. CoRR abs/2010.08221 (2020) - [i13]René Schuster, Christian Unger, Didier Stricker:
MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow. CoRR abs/2010.10842 (2020) - [i12]René Schuster, Christian Unger, Didier Stricker:
A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions. CoRR abs/2011.01603 (2020)
2010 – 2019
- 2019
- [c13]René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker:
An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching. CVPR Workshops 2019: 1344-1352 - [c12]René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker:
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks. CVPR 2019: 2556-2565 - [c11]Ramy Battrawy, René Schuster, Oliver Wasenmüller, Qing Rao, Didier Stricker:
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images. IROS 2019: 7762-7769 - [c10]Rohan Saxena, René Schuster, Oliver Wasenmüller, Didier Stricker:
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation. IV 2019: 324-331 - [i11]René Schuster, Oliver Wasenmüller, Christian Unger, Georg Kuschk, Didier Stricker:
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation. CoRR abs/1902.10099 (2019) - [i10]René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker:
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks. CoRR abs/1904.03076 (2019) - [i9]Rohan Saxena, René Schuster, Oliver Wasenmüller, Didier Stricker:
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation. CoRR abs/1904.06116 (2019) - [i8]René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker:
An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching. CoRR abs/1904.06167 (2019) - [i7]Ramy Battrawy, René Schuster, Oliver Wasenmüller, Qing Rao, Didier Stricker:
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images. CoRR abs/1910.14453 (2019) - 2018
- [c9]René Schuster, Christian Bailer, Oliver Wasenmüller, Didier Stricker:
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. ICIP 2018: 1463-1467 - [c8]Patrik Feth, Mohammed Naveed Akram, René Schuster, Oliver Wasenmüller:
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning. SAFECOMP Workshops 2018: 535-547 - [c7]René Schuster, Oliver Wasenmüller, Georg Kuschk, Christian Bailer, Didier Stricker:
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences. WACV 2018: 1056-1065 - [i6]René Schuster, Christian Bailer, Oliver Wasenmüller, Didier Stricker:
Combining Stereo Disparity and Optical Flow for Basic Scene Flow. CoRR abs/1801.04720 (2018) - [i5]René Schuster, Christian Bailer, Oliver Wasenmüller, Didier Stricker:
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation. CoRR abs/1805.03517 (2018) - [i4]Patrik Feth, Mohammed Naveed Akram, René Schuster, Oliver Wasenmüller:
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning. CoRR abs/1806.07635 (2018) - [i3]René Schuster, Oliver Wasenmüller, Didier Stricker:
Dense Scene Flow from Stereo Disparity and Optical Flow. CoRR abs/1808.10146 (2018) - [i2]Oliver Wasenmüller, René Schuster, Didier Stricker, Karl Leiss, Jürger Pfister, Oleksandra Ganus, Julian Tatsch, Artem Savkin, Nikolas Brasch:
Automated Scene Flow Data Generation for Training and Verification. CoRR abs/1808.10232 (2018) - 2017
- [i1]René Schuster, Oliver Wasenmüller, Georg Kuschk, Christian Bailer, Didier Stricker:
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences. CoRR abs/1710.10096 (2017) - 2012
- [c6]Roland Mörzinger, René Schuster, Andras Horti, Georg Thallinger:
Visual Structure Analysis of Flow Charts in Patent Images. CLEF (Online Working Notes/Labs/Workshop) 2012 - [c5]Mihai Lupu, René Schuster, Roland Mörzinger, Florina Piroi, Tobias Schleser, Allan Hanbury:
Patent images - a glass-encased tool: opening the case. I-KNOW 2012: 16 - 2011
- [c4]Josef A. Birchbauer, Samuel Schulter, René Schuster, Georg Poier, Martin Winter, Peter Schallauer, Peter M. Roth, Horst Bischof:
AVSS 2011 demo session: OUTLIER - online learning and visualization of unusual events. AVSS 2011: 533-534 - [c3]René Schuster, Samuel Schulter, Georg Poier, Martin Hirzer, Josef A. Birchbauer, Peter M. Roth, Horst Bischof, Martin Winter, Peter Schallauer:
Multi-cue learning and visualization of unusual events. ICCV Workshops 2011: 1933-1940 - 2010
- [c2]Roland Mörzinger, Manolis Sardis, Igor Rosenberg, Helmut Grabner, Galina V. Veres, Imed Bouchrika, Marcus Thaler, René Schuster, Albert Hofmann, Georg Thallinger, Vasileios Anagnostopoulos, Dimitrios I. Kosmopoulos, Athanasios Voulodimos, Constantinos Lalos, Nikolaos D. Doulamis, Theodora A. Varvarigou, Rolando Palma Zelada, Ignacio Jubert Soler, Severin Stalder, Luc Van Gool, Lee Middleton, Zoheir A. Sabeur, Banafshe Arbab-Zavar, John N. Carter, Mark S. Nixon:
Tools for semi-automatic monitoring of industrial workflows. ARTEMIS@ACM Multimedia 2010: 81-86 - [c1]René Schuster, Roland Mörzinger, Werner Haas, Helmut Grabner, Luc Van Gool:
Real-time detection of unusual regions in image streams. ACM Multimedia 2010: 1307-1310
Coauthor Index
[j2] [c37] [c36] [i29] [i28] [i27] [i26] [c35] [c34] [c33] [c32] [c31] [c30] [i25] [i24] [c26] [c25] [c24] [i23] [i22] [i21] [i20] [i19] [c23] [c21] [c20] [i18] [j1] [c19] [c17] [c16] [c15] [c14] [i17] [i16] [i15] [i14] [i13] [i12] [c13] [c12] [c11] [c10] [i11] [i10] [i9] [i8] [i7] [c9] [c7] [i6] [i5] [i3] [i2] [i1]
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last updated on 2024-10-07 21:23 CEST by the dblp team
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