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Larissa T. Triess
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2020 – today
- 2023
- [b1]Larissa T. Triess:
LiDAR Domain Adaptation - Automotive 3D Scene Understanding. Karlsruhe Institute of Technology, Germany, 2023 - 2022
- [j1]Larissa T. Triess
, Christoph B. Rist
A Realism Metric for Generated LiDAR Point Clouds. Int. J. Comput. Vis. 130(12): 2962-2979 (2022) - [c5]Larissa T. Triess, Andre Bühler, David Peter
Point Cloud Generation with Continuous Conditioning. AISTATS 2022: 4462-4481 - [i7]Larissa T. Triess, Andre Bühler, David Peter, Fabian B. Flohr, J. Marius Zöllner:
Point Cloud Generation with Continuous Conditioning. CoRR abs/2202.08526 (2022) - [i6]Larissa T. Triess
A Realism Metric for Generated LiDAR Point Clouds. CoRR abs/2208.14958 (2022) - 2021
- [c4]Larissa T. Triess
Quantifying Point Cloud Realism Through Adversarially Learned Latent Representations. GCPR 2021: 681-696 - [c3]Larissa T. Triess
A Survey on Deep Domain Adaptation for LiDAR Perception. IV Workshops 2021: 350-357 - [i5]Larissa T. Triess
A Survey on Deep Domain Adaptation for LiDAR Perception. CoRR abs/2106.02377 (2021) - [i4]Larissa T. Triess
Quantifying point cloud realism through adversarially learned latent representations. CoRR abs/2109.11775 (2021) - [i3]Larissa T. Triess
Semi-Local Convolutions for LiDAR Scan Processing. CoRR abs/2111.15615 (2021) - 2020
- [c2]Larissa T. Triess
Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study. IV 2020: 1116-1121 - [i2]Larissa T. Triess
Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study. CoRR abs/2004.11803 (2020)
2010 – 2019
- 2019
- [c1]Larissa T. Triess
CNN-based synthesis of realistic high-resolution LiDAR data. IV 2019: 1512-1519 - [i1]Larissa T. Triess
CNN-based synthesis of realistic high-resolution LiDAR data. CoRR abs/1907.00787 (2019)
Coauthor Index
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