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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.17378 |
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| _version_ | 1866909603945512960 |
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| author | Gui, Hao Hu, Lin Chen, Rui Huang, Mingxiao Yin, Yuxin Yang, Jin Wu, Yong Liu, Chen Sun, Zhongxu Zhang, Xueyang Zhan, Kun |
| author_facet | Gui, Hao Hu, Lin Chen, Rui Huang, Mingxiao Yin, Yuxin Yang, Jin Wu, Yong Liu, Chen Sun, Zhongxu Zhang, Xueyang Zhan, Kun |
| contents | 3D Gaussian Splatting (3DGS) is increasingly attracting attention in both academia and industry owing to its superior visual quality and rendering speed. However, training a 3DGS model remains a time-intensive task, especially in load imbalance scenarios where workload diversity among pixels and Gaussian spheres causes poor renderCUDA kernel performance. We introduce Balanced 3DGS, a Gaussian-wise parallelism rendering with fine-grained tiling approach in 3DGS training process, perfectly solving load-imbalance issues. First, we innovatively introduce the inter-block dynamic workload distribution technique to map workloads to Streaming Multiprocessor(SM) resources within a single GPU dynamically, which constitutes the foundation of load balancing. Second, we are the first to propose the Gaussian-wise parallel rendering technique to significantly reduce workload divergence inside a warp, which serves as a critical component in addressing load imbalance. Based on the above two methods, we further creatively put forward the fine-grained combined load balancing technique to uniformly distribute workload across all SMs, which boosts the forward renderCUDA kernel performance by up to 7.52x. Besides, we present a self-adaptive render kernel selection strategy during the 3DGS training process based on different load-balance situations, which effectively improves training efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17378 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Balanced 3DGS: Gaussian-wise Parallelism Rendering with Fine-Grained Tiling Gui, Hao Hu, Lin Chen, Rui Huang, Mingxiao Yin, Yuxin Yang, Jin Wu, Yong Liu, Chen Sun, Zhongxu Zhang, Xueyang Zhan, Kun Computer Vision and Pattern Recognition 3D Gaussian Splatting (3DGS) is increasingly attracting attention in both academia and industry owing to its superior visual quality and rendering speed. However, training a 3DGS model remains a time-intensive task, especially in load imbalance scenarios where workload diversity among pixels and Gaussian spheres causes poor renderCUDA kernel performance. We introduce Balanced 3DGS, a Gaussian-wise parallelism rendering with fine-grained tiling approach in 3DGS training process, perfectly solving load-imbalance issues. First, we innovatively introduce the inter-block dynamic workload distribution technique to map workloads to Streaming Multiprocessor(SM) resources within a single GPU dynamically, which constitutes the foundation of load balancing. Second, we are the first to propose the Gaussian-wise parallel rendering technique to significantly reduce workload divergence inside a warp, which serves as a critical component in addressing load imbalance. Based on the above two methods, we further creatively put forward the fine-grained combined load balancing technique to uniformly distribute workload across all SMs, which boosts the forward renderCUDA kernel performance by up to 7.52x. Besides, we present a self-adaptive render kernel selection strategy during the 3DGS training process based on different load-balance situations, which effectively improves training efficiency. |
| title | Balanced 3DGS: Gaussian-wise Parallelism Rendering with Fine-Grained Tiling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.17378 |