17 citations · 19 across the 7 of their papers we have counts for
7 papers
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…
MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors
Chengzhi Wu, Kanran Zhou, Jan-Philipp Kaiser +7
To enable automatic disassembly of different product types with uncertain conditions and degrees of wear in remanufacturing, agile production systems that can adapt dynamically to…
Radar Odometry on SE(3) with Constant Acceleration Motion Prior and Polar Measurement Model
Kyle Retan, Frasher Loshaj, Michael Heizmann
This paper presents an approach to radar odometry on which utilizes a constant acceleration motion prior. The motion prior is integrated into a sliding window optimization…
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…