Ted Lentsch

Emergent 3D Instance Segmentation from Self-Supervised Point Transformers

This work investigates whether a frozen, self-supervised point transformer already contains the structural information required to isolate object instances without any handcrafted geometric prior, and develops a training-free segmenter that groups points via connected components on a key-similarity graph, using neither density-based clustering nor proximity priors.