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Research Article | Open Access

Swin3D: A pretrained transformer backbone for 3D indoor scene

Institute for Advanced Study, Tsinghua University, Beijing 100084, China
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Internet Graphics Group, Microsoft Research Asia, Beijing 100080, China
Wangxuan Institute of Computer Technology, Peking University, Beijing 100080, China
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Graphical Abstract

Abstract

The use of pretrained backbones with fine-tuning has shown success for 2D vision and natural language processing tasks, with advantages over task-specific networks. In this paper, we introduce a pretrained 3D backbone, called Swin3D, for 3D indoor scene understanding. We designed a 3D Swin Transformer as our backbone network, which enables efficient self-attention on sparse voxels with linear memory complexity, making the backbone scalable to large models and datasets. We also introduce a generalized contextual relative positional embedding scheme to capture various irregularities of point signals for improved network performance. We pretrained a large Swin3D model on a synthetic Structured3D dataset, which is an order of magnitude larger than the ScanNet dataset. Our model pretrained on the synthetic dataset not only generalizes well to downstream segmentation and detection on real 3D point datasets but also outperforms state-of-the-art methods on downstream tasks with +2.3 mIoU and +2.2 mIoU on S3DIS Area5 and 6-fold semantic segmentation, respectively, +1.8 mIoU on ScanNet segmentation (val), +1.9 mAP@0.5 on ScanNet detection, and +8.1 mAP@0.5 on S3DIS detection. A series of extensive ablation studies further validated the scalability, generality, and superior performance enabled by our approach.

Keywords

3D pretrainingponitcloud analysistransformer backboneSwin Transformer3D semantic segmentation3D object detection

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Computational Visual Media
Volume 11 Issue 1,
February 2025
Pages 83-101
DOI: 10.26599/CVM.2025.9450383
Cite this article:
Yang Y-Q, Guo Y-X, Xiong J-Y, et al. Swin3D: A pretrained transformer backbone for 3D indoor scene. Computational Visual Media, 2025, 11(1): 83-101. https://doi.org/10.26599/CVM.2025.9450383

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Received: 06 August 2023
Accepted: 01 October 2023
Published: 28 February 2025
© The Author(s) 2025.

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