3 papers
cs.CV2023
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…
cs.CV2023
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…
cs.CV2022
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…