Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation (2312.07231v1)

Abstract: Diffusion Transformers have recently shown remarkable effectiveness in generating high-quality 3D point clouds. However, training voxel-based diffusion models for high-resolution 3D voxels remains prohibitively expensive due to the cubic complexity of attention operators, which arises from the additional dimension of voxels. Motivated by the inherent redundancy of 3D compared to 2D, we propose FastDiT-3D, a novel masked diffusion transformer tailored for efficient 3D point cloud generation, which greatly reduces training costs. Specifically, we draw inspiration from masked autoencoders to dynamically operate the denoising process on masked voxelized point clouds. We also propose a novel voxel-aware masking strategy to adaptively aggregate background/foreground information from voxelized point clouds. Our method achieves state-of-the-art performance with an extreme masking ratio of nearly 99%. Moreover, to improve multi-category 3D generation, we introduce Mixture-of-Expert (MoE) in 3D diffusion model. Each category can learn a distinct diffusion path with different experts, relieving gradient conflict. Experimental results on the ShapeNet dataset demonstrate that our method achieves state-of-the-art high-fidelity and diverse 3D point cloud generation performance. Our FastDiT-3D improves 1-Nearest Neighbor Accuracy and Coverage metrics when generating 128-resolution voxel point clouds, using only 6.5% of the original training cost.

• The paper introduces FastDiT-3D, a diffusion transformer that uses extreme voxel-aware masking to efficiently generate high-quality 3D point clouds.
• It integrates Mixture-of-Experts layers to handle multi-category denoising and achieves superior 1-NNA and COV metrics with only 6.5% of the original training cost.
• The model demonstrates scalability on the ShapeNet dataset, paving the way for real-time 3D applications in autonomous driving, VR, and advanced 3D modeling.

Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation

Overview and Methodology

The paper, "Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation," presents a novel approach for generating high-quality 3D point clouds efficiently using Diffusion Transformers. The proposed method, FastDiT-3D, introduces innovative techniques to address the computational challenges associated with training voxel-based diffusion models, especially at high resolutions. The primary contributions can be summarized as follows:

1. The introduction of FastDiT-3D, a masked diffusion transformer optimized for 3D point cloud generation.
2. A novel voxel-aware masking strategy that achieves an extreme masking ratio of nearly 99% to dynamically operate the denoising process on masked voxelized point clouds.
3. Integration of Mixture-of-Experts (MoE) layers within the transformer to manage multiple categories effectively.
4. State-of-the-art performance in terms of 1-NNA and Coverage (COV) metrics, using only 6.5% of the original training cost.

The FastDiT-3D leverages the inherent redundancy in 3D data compared to 2D, applying principles from masked autoencoders to the voxelized point cloud domain. This not only reduces the computational burden but also maintains high fidelity in generated samples.

Experimental Results

The performance of FastDiT-3D is empirically validated on the ShapeNet dataset. The experimental results indicate that FastDiT-3D outperforms existing methods in various metrics:

• 1-Nearest Neighbor Accuracy (1-NNA) and Coverage (COV): FastDiT-3D consistently achieves superior results, with significant improvements over state-of-the-art models like DiT-3D and LION.
• Training Efficiency: The model reduces the training costs dramatically; for instance, generating 128-resolution voxel point clouds uses only 108 A100 GPU hours compared to 1668 hours required by DiT-3D.

Technical Highlights

Key technical elements of FastDiT-3D include:

• Voxel-Aware Masking: This strategy involves separating the voxel data into foreground (occupied) and background (non-occupied) regions, applying different masking ratios to maximize efficiency. The paper reports that the foreground-background aware masking mechanism leads to an extreme masking ratio with only 327 unmasked tokens out of the original 32,768 for a voxel size of 128 × 128 × 128.
• Encoder-Decoder Architecture: The encoder utilizes global multi-head self-attention to process the unmasked patches while the decoder employs 3D window attention to manage the computational complexity.
• Mixture-of-Experts (MoE): The inclusion of MoE layers in the transformer model addresses the gradient conflict in multi-category 3D point cloud generation. Each category can learn distinct diffusion paths comprising different sets of experts, thus optimizing the learning process for diverse categories.

Implications and Future Work

The proposed FastDiT-3D has significant practical and theoretical implications:

• Practical Implications: The reduction in training cost makes it feasible to scale up models to higher resolutions and larger datasets without prohibitive computational resources. This is particularly beneficial for applications in autonomous driving, virtual reality, and 3D modeling where high-quality 3D data generation is crucial.
• Theoretical Implications: The work demonstrates the effectiveness of extreme masking strategies and MoE in reducing computational overhead while maintaining or improving model performance. This could inspire further research into sparse representations and efficient learning mechanisms in high-dimensional data spaces.

Looking ahead, the research opens several avenues for future development:

• Extension to Other Modalities: The methodologies introduced could be extended to other 3D data types, such as meshes and point clouds with additional attributes (e.g., color, texture).
• Integration with Textual Descriptions: Combining the FastDiT-3D with natural language processing techniques could enable text-to-3D generation, broadening the scope of applications.
• Real-time Applications: Improving the inference speed of the model could pave the way for real-time applications in dynamic environments.

Conclusion

The FastDiT-3D framework represents a substantial advancement in the field of 3D point cloud generation, achieving state-of-the-art performance efficiently. Its novel use of extreme masking and voxel-aware strategies, along with the integration of Mixture-of-Experts, not only reduces computational requirements but also enhances the quality and diversity of the generated 3D shapes. These contributions are likely to have a lasting impact on both theoretical research and practical applications, driving further innovations in 3D data generation and beyond.