PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

TL;DR

This paper demonstrates that unsupervised pre-training significantly improves 3D point cloud understanding tasks across multiple benchmarks, comparable to supervised methods, and highlights the potential of scaling data collection over annotation.

Contribution

It introduces a unified contrastive pre-training approach for 3D point clouds that enhances performance on high-level scene understanding tasks across diverse datasets.

Findings

Unsupervised pre-training improves segmentation and detection results.

Pre-training generalizes well across indoor and outdoor, real and synthetic datasets.

Scaling data collection may be more beneficial than detailed annotation.

Abstract

Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (eg., ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been instrumental to many applications in language and vision. Yet, very little is known about its usefulness in 3D point cloud understanding. We see this as an opportunity considering the effort required for annotating data in 3D. In this work, we aim at facilitating research on 3D representation learning. Different from previous works, we focus on high-level scene understanding tasks. To this end, we select a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes. Our findings are extremely encouraging: using a unified triplet of architecture, source dataset, and contrastive loss for pre-training, we achieve improvement over recent best results in segmentation and detection across 6 different benchmarks for indoor and outdoor, real and synthetic datasets -- demonstrating that the learned representation can generalize across domains. Furthermore, the improvement was similar to supervised pre-training, suggesting that future efforts should favor scaling data collection over more detailed annotation. We hope these findings will encourage more research on unsupervised pretext task design for 3D deep learning.