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cs.CV2025
DOS: Distilling Observable Softmaps of Zipfian Prototypes for Self-Supervised Point Representation
Mohamed Abdelsamad, Michael Ulrich, Bin Yang +3
Recent advances in self-supervised learning (SSL) have shown tremendous potential for learning 3D point cloud representations without human annotations. However, SSL for 3D point c…
cs.CV2025
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds
Mohamed Abdelsamad, Michael Ulrich, Claudius Gläser +1
Masked autoencoders (MAE) have shown tremendous potential for self-supervised learning (SSL) in vision and beyond. However, point clouds from LiDARs used in automated driving are p…