Pretrained backbones with fine-tuning have been widely adopted in 2D vision and natural language
processing tasks and demonstrated significant advantages to task-specific networks. In this paper, we
present a pretrained 3D backbone, named Swin3D, which first outperforms all state-of-the-art methods in
downstream 3D indoor scene understanding tasks. Our backbone network is based on a 3D Swin transformer and
carefully designed to efficiently conduct self-attention on sparse voxels with linear memory complexity
and capture the irregularity of point signals via generalized contextual relative positional embedding.
Based on this backbone design, we pretrained a large Swin3D model on a synthetic Structured3D dataset that
is 10 times larger than the ScanNet dataset and fine-tuned the pretrained model in various downstream
real-world indoor scene understanding tasks. The results demonstrate that our model pretrained on the
synthetic dataset not only exhibits good generality in both downstream segmentation and detection on real
3D point datasets, but also surpasses the state-of-the-art methods on downstream tasks after fine-tuning
with +2.3 mIoU and +2.2 mIoU on S3DIS Area5 and 6-fold semantic segmentation, +2.1 mIoU on ScanNet
segmentation (val), +1.9 mAP@0.5 on ScanNet detection, +8.1 mAP@0.5 on S3DIS detection. Our method
demonstrates the great potential of pretrained 3D backbones with fine-tuning for 3D understanding tasks.
