Exploring Transfer Learning with Point Transformers: Evaluating Classification Performance on ModelNet10 and 3D MNIST Datasets

Point Transformers, a self-attention based architecture, can effectively capture spatial dependencies in point cloud data and achieve near state-of-the-art performance on various 3D tasks. However, the transfer learning capabilities of these models are limited when the source and target datasets have significantly different underlying data distributions.