Point Transformers are near state-of-the-art models for classification,
segmentation, and detection tasks on Point Cloud data. They utilize a self
attention based mechanism to model large range spatial dependencies between
multiple point sets. In this project we explore two things: classification
performance of these attention based networks on ModelNet10 dataset and then,
we use the trained model to classify 3D MNIST dataset after finetuning. We also
train the model from scratch on 3D MNIST dataset to compare the performance of
finetuned and from-scratch model on the MNIST dataset. We observe that since
the two datasets have a large difference in the degree of the distributions,
transfer learned models do not outperform the from-scratch models in this case.
Although we do expect transfer learned models to converge faster since they
already know the lower level edges, corners, etc features from the ModelNet10
dataset.

基于 Point Transformers 模型的分类性能在 ModelNet10 数据集上表现良好，并在 fine-tuning 后用于 3D MNIST 数据集分类，在这种情况下，迁移学习模型没有超过基于 3D MNIST 数据集从头开始训练的模型，虽然我们预期迁移学习模型收敛更快，因为它们已经了解了 ModelNet10 数据集中的边缘、角点等低层特征。