  We present SeRP, a framework for Self-Supervised Learning of 3D point clouds.
SeRP consists of encoder-decoder architecture that takes perturbed or corrupted
point clouds as inputs and aims to reconstruct the original point cloud without
corruption. The encoder learns the high-level latent representations of the
points clouds in a low-dimensional subspace and recovers the original
structure. In this work, we have used Transformers and PointNet-based
Autoencoders. The proposed framework also addresses some of the limitations of
Transformers-based Masked Autoencoders which are prone to leakage of location
information and uneven information density. We trained our models on the
complete ShapeNet dataset and evaluated them on ModelNet40 as a downstream
classification task. We have shown that the pretrained models achieved 0.5-1%
higher classification accuracies than the networks trained from scratch.
Furthermore, we also proposed VASP: Vector-Quantized Autoencoder for
Self-supervised Representation Learning for Point Clouds that employs
Vector-Quantization for discrete representation learning for Transformer-based
autoencoders.
