Boosting LiDAR-Based Semantic Labeling by Cross-modal Training Data Generation. - Florian Piewak; Peter Pinggera; Manuel Schäfer; David Peter; Beate Schwarz; Nick Schneider; Markus Enzweiler; David Pfeiffer; J. Marius Zöllner (ECCV Workshops (6), 2018)

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

Resource-Efficient DNNs for Keyword Spotting using Neural Architecture Search and Quantization. - David Peter; Wolfgang Roth; Franz Pernkopf (ICPR, 2020)

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)

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)

End-to-End Keyword Spotting Using Neural Architecture Search and Quantization. - David Peter; Wolfgang Roth; Franz Pernkopf (ICASSP, 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)

Systematic Selection of a Suitable Data Imputation Technique Based on Data Characteristics. - Anu Maria Sebastian; David Peter (SN Comput. Sci., 2025)