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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.24742 |
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| _version_ | 1866908741333417984 |
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| author | Liu, Xiang Zhou, Yimin Wang, Jinxiang Huang, Yujun Xie, Shuzhao Qin, Shiyu Hong, Mingyao Li, Jiawei Wang, Yaowei Wang, Zhi Xia, Shu-Tao Chen, Bin |
| author_facet | Liu, Xiang Zhou, Yimin Wang, Jinxiang Huang, Yujun Xie, Shuzhao Qin, Shiyu Hong, Mingyao Li, Jiawei Wang, Yaowei Wang, Zhi Xia, Shu-Tao Chen, Bin |
| contents | The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms has created a pressing need for standardized and comprehensive evaluation tools, especially for compression task. Existing benchmarks often lack the specific metrics necessary to holistically assess the unique characteristics of different methods, such as rendering speed, rate distortion trade-offs memory efficiency, and geometric accuracy. To address this gap, we introduce Splatwizard, a unified benchmark toolkit designed specifically for benchmarking 3DGS compression models. Splatwizard provides an easy-to-use framework to implement new 3DGS compression model and utilize state-of-the-art techniques proposed by previous work. Besides, an integrated pipeline that automates the calculation of key performance indicators, including image-based quality metrics, chamfer distance of reconstruct mesh, rendering frame rates, and computational resource consumption is included in the framework as well. Code is available at https://github.com/splatwizard/splatwizard |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24742 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression Liu, Xiang Zhou, Yimin Wang, Jinxiang Huang, Yujun Xie, Shuzhao Qin, Shiyu Hong, Mingyao Li, Jiawei Wang, Yaowei Wang, Zhi Xia, Shu-Tao Chen, Bin Computer Vision and Pattern Recognition The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms has created a pressing need for standardized and comprehensive evaluation tools, especially for compression task. Existing benchmarks often lack the specific metrics necessary to holistically assess the unique characteristics of different methods, such as rendering speed, rate distortion trade-offs memory efficiency, and geometric accuracy. To address this gap, we introduce Splatwizard, a unified benchmark toolkit designed specifically for benchmarking 3DGS compression models. Splatwizard provides an easy-to-use framework to implement new 3DGS compression model and utilize state-of-the-art techniques proposed by previous work. Besides, an integrated pipeline that automates the calculation of key performance indicators, including image-based quality metrics, chamfer distance of reconstruct mesh, rendering frame rates, and computational resource consumption is included in the framework as well. Code is available at https://github.com/splatwizard/splatwizard |
| title | Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.24742 |