  As a pioneering work, PointContrast conducts unsupervised 3D representation
learning via leveraging contrastive learning over raw RGB-D frames and proves
its effectiveness on various downstream tasks. However, the trend of
large-scale unsupervised learning in 3D has yet to emerge due to two stumbling
blocks: the inefficiency of matching RGB-D frames as contrastive views and the
annoying mode collapse phenomenon mentioned in previous works. Turning the two
stumbling blocks into empirical stepping stones, we first propose an efficient
and effective contrastive learning framework, which generates contrastive views
directly on scene-level point clouds by a well-curated data augmentation
pipeline and a practical view mixing strategy. Second, we introduce
reconstructive learning on the contrastive learning framework with an exquisite
design of contrastive cross masks, which targets the reconstruction of point
color and surfel normal. Our Masked Scene Contrast (MSC) framework is capable
of extracting comprehensive 3D representations more efficiently and
effectively. It accelerates the pre-training procedure by at least 3x and still
achieves an uncompromised performance compared with previous work. Besides, MSC
also enables large-scale 3D pre-training across multiple datasets, which
further boosts the performance and achieves state-of-the-art fine-tuning
results on several downstream tasks, e.g., 75.5% mIoU on ScanNet semantic
segmentation validation set.
