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.

本研究旨在促进 3D 深度学习表示学习的研究，重点关注高级场景理解任务，使用统一的三元组架构、源数据集和对比损失进行无监督预训练，对室内和室外、真实和合成数据集的 6 个不同基准进行分割和检测，取得了鼓舞性的结果，表明学习到的表示可以在不同领域推广使用，并且与监督的预训练的改善程度相似，建议未来的努力应该更加注重扩充数据收集而非详细标注。

PointContrast: 无监督预训练 3D 点云理解