Hierarchical abstraction drives human-like 3-D shape processing in deep learning models
Fig 9
Scrambled stimuli used in Experiment 3.
Each row shows five objects (airplane, car, chair, lamp, and table) in either the original (top row) or scrambled (bottom row) condition. Scrambling was performed separately for DGCNN and Point Transformer models while preserving part identity.