Scaling Point-based Differentiable Rendering for Large-scale Reconstruction
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908730072760320 |
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| author | Zhao, Hexu Liu, Xiaoteng Min, Xiwen Huang, Jianhao Deng, Youming Li, Yanfei Li, Ang Li, Jinyang Panda, Aurojit |
| author_facet | Zhao, Hexu Liu, Xiaoteng Min, Xiwen Huang, Jianhao Deng, Youming Li, Yanfei Li, Ang Li, Jinyang Panda, Aurojit |
| contents | Point-based Differentiable Rendering (PBDR) enables high-fidelity 3D scene reconstruction, but scaling PBDR to high-resolution and large scenes requires efficient distributed training systems. Existing systems are tightly coupled to a specific PBDR method. And they suffer from severe communication overhead due to poor data locality. In this paper, we present Gaian, a general distributed training system for PBDR. Gaian provides a unified API expressive enough to support existing PBDR methods, while exposing rich data-access information, which Gaian leverages to optimize locality and reduce communication. We evaluated Gaian by implementing 4 PBDR algorithms. Our implementations achieve high performance and resource efficiency: across six datasets and up to 128 GPUs, it reduces communication by up to 91% and improves training throughput by 1.50x-3.71x. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scaling Point-based Differentiable Rendering for Large-scale Reconstruction Zhao, Hexu Liu, Xiaoteng Min, Xiwen Huang, Jianhao Deng, Youming Li, Yanfei Li, Ang Li, Jinyang Panda, Aurojit Distributed, Parallel, and Cluster Computing Graphics C.0; I.3.2; I.4.5 Point-based Differentiable Rendering (PBDR) enables high-fidelity 3D scene reconstruction, but scaling PBDR to high-resolution and large scenes requires efficient distributed training systems. Existing systems are tightly coupled to a specific PBDR method. And they suffer from severe communication overhead due to poor data locality. In this paper, we present Gaian, a general distributed training system for PBDR. Gaian provides a unified API expressive enough to support existing PBDR methods, while exposing rich data-access information, which Gaian leverages to optimize locality and reduce communication. We evaluated Gaian by implementing 4 PBDR algorithms. Our implementations achieve high performance and resource efficiency: across six datasets and up to 128 GPUs, it reduces communication by up to 91% and improves training throughput by 1.50x-3.71x. |
| title | Scaling Point-based Differentiable Rendering for Large-scale Reconstruction |
| topic | Distributed, Parallel, and Cluster Computing Graphics C.0; I.3.2; I.4.5 |
| url | https://arxiv.org/abs/2512.20017 |