  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.

Computer Vision and Pattern Recognition