Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding (2304.06906v3)

Abstract: The use of pretrained backbones with fine-tuning has been successful for 2D vision and natural language processing tasks, showing advantages over task-specific networks. In this work, we introduce a pretrained 3D backbone, called {\SST}, for 3D indoor scene understanding. We design 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 {\SST} 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, +1.8 mIoU on ScanNet segmentation (val), +1.9 [email protected] on ScanNet detection, and +8.1 [email protected] on S3DIS detection. A series of extensive ablation studies further validate the scalability, generality, and superior performance enabled by our approach. The code and models are available at https://github.com/microsoft/Swin3D .

• The paper introduces a novel transformer architecture adapted from the Swin design for efficient 3D indoor scene understanding.
• It implements a memory-efficient self-attention mechanism using sparse voxels to overcome quadratic memory complexity.
• The pretrained model shows improved mIoU and mAP on benchmarks like ScanNet and S3DIS, setting new standards in 3D segmentation and detection.

Overview of Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

The paper "Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding" introduces a novel pretrained backbone specifically designed for 3D scene understanding tasks. The backbone, referred to as Swin3D or {\SST}, addresses the primary challenges of memory complexity and signal irregularity in 3D transformers by utilizing sparse voxels, linear memory complexity, and generalized contextual relative positional embedding.

Key Contributions

1. Transformer Architecture Adaptation: The authors have adapted the Swin Transformer design, originally developed for 2D images, to be applicable to 3D unorganized point clouds. This adaptation allows for efficient self-attention computation on sparse voxels.
2. Efficiency in Self-Attention: By developing a memory-efficient self-attention mechanism, {\SST} overcomes the quadratic memory complexity barrier. The proposed approach reduces the quadratic growth in memory consumption typical of standard self-attention, enabling the exploration of larger models.
3. Generalized Contextual Relative Positional Embedding: The paper generalizes the notion of contextual relative positional encoding (cRPE) to a more comprehensive contextual relative signal encoding (cRSE). This enhancement captures both the spatial and signal irregularities inherent in 3D point cloud data.
4. Extensive Pretraining and Evaluation: {\SST} is pretrained on the Structured3D dataset, which is significantly larger than previous datasets used for 3D scene understanding, showing the ability of the model to generalize. The model is further evaluated on real 3D datasets such as ScanNet and S3DIS, demonstrating superior performance compared to state-of-the-art methods in semantic segmentation and 3D detection tasks.

Performance and Experimental Results

The experimental results of {\SST} are notable for several reasons:

• Segmentational Improvements: On semantic segmentation tasks within the ScanNet and S3DIS datasets, {\SST} consistently outperforms existing state-of-the-art methods, achieving notable improvements in mean Intersection over Union (mIoU).
• Detection Enhancements: In 3D detection, the Swin3D backbone further advances the accuracy measured by mean Average Precision (mAP), particularly at higher IoU thresholds (e.g., +8.1 [email protected] on S3DIS detection), showcasing the advantages of a pretrained transformer backbone.
• Scalability: The paper highlights the scalability of {\SST} regarding its performance enhancements when benefiting from increased model capacity and training data size. This is achieved while efficiently managing memory consumption, a critical consideration for 3D point clouds.

Implications and Future Directions

The implications of the proposed Swin3D model are manifold:

• Unified 3D Understanding Framework: The introduction of a unified pretrained transformer backbone applicable across various 3D vision tasks underscores the potential for transformer models to become the standard architecture across different domains, akin to their success in 2D computer vision and NLP.
• Data Efficiency and Model Generality: By focusing on pretraining with a broader dataset and fine-tuning for task specificity, Swin3D showcases the potential for reduced reliance on large labeled datasets, a critical bottleneck in current supervised learning paradigms.
• Potential Extensions: Considering the evolving landscape of AI, further exploration into integrating Swin3D with other modalities, such as visual and acoustic data, could extend its applicability to richer and more diverse environments, such as autonomous navigation and complex interactive systems.

The paper concludes with an open-source release of their code and pretrained models, encouraging further research and application in the field of 3D vision, which provides a valuable resource for continued innovation and development.