17 citations · 19 across the 7 of their papers we have counts for
5 papers · 1 filter
Randomized 3D Scene Generation for Generalizable Self-Supervised Pre-Training
Lanxiao Li, Michael Heizmann
Capturing and labeling real-world 3D data is laborious and time-consuming, which makes it costly to train strong 3D models. To address this issue, recent works present a simple met…
Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences
Ahmed J. Afifi, Samuel T. Thiele, Aldino Rizaldy +6
The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop…
Applying Plain Transformers to Real-World Point Clouds
Lanxiao Li, Michael Heizmann
To apply transformer-based models to point cloud understanding, many previous works modify the architecture of transformers by using, e.g., local attention and down-sampling. Altho…
A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision
Lanxiao Li, Michael Heizmann
Self-supervised pre-training for 3D vision has drawn increasing research interest in recent years. In order to learn informative representations, a lot of previous works exploit in…
Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning
Maximilian Schambach, Jiayang Shi, Michael Heizmann
We present a novel method to reconstruct a spectral central view and its aligned disparity map from spatio-spectrally coded light fields. Since we do not reconstruct an intermediat…