CNN-based synthesis of realistic high-resolution LiDAR data. - Larissa T. Triess; David Peter; Christoph B. Rist; Markus Enzweiler; J. Marius Zöllner (IV, 2019)

Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study. - Larissa T. Triess; David Peter; Christoph B. Rist; J. Marius Zöllner (IV, 2020)

Quantifying Point Cloud Realism Through Adversarially Learned Latent Representations. - Larissa T. Triess; David Peter; Stefan A. Baur; J. Marius Zöllner (GCPR, 2021)

A Survey on Deep Domain Adaptation for LiDAR Perception. - Larissa T. Triess; Mariella Dreissig; Christoph B. Rist; J. Marius Zöllner (IV Workshops, 2021)

Semi-Local Convolutions for LiDAR Scan Processing. - Larissa T. Triess; David Peter; J. Marius Zöllner (CoRR, 2021)

Point Cloud Generation with Continuous Conditioning. - Larissa T. Triess; Andre Bühler; David Peter; Fabian B. Flohr; Marius Zöllner 001 (AISTATS, 2022)

A Realism Metric for Generated LiDAR Point Clouds. - Larissa T. Triess; Christoph B. Rist; David Peter; J. Marius Zöllner (Int. J. Comput. Vis., 2022)

LiDAR Domain Adaptation - Automotive 3D Scene Understanding. - Larissa T. Triess (2023)