Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering
This study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of various performance indicators under differing res...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/1/21 |
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| author | Xinjian Fang Yingdan Zhang Hao Tan Chao Liu Xu Yang |
| author_facet | Xinjian Fang Yingdan Zhang Hao Tan Chao Liu Xu Yang |
| author_sort | Xinjian Fang |
| collection | DOAJ |
| description | This study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of various performance indicators under differing resolution conditions, threshold settings for the average magnitude of spatial position gradients, and adjustments to the scaling learning rate, the optimal parameter configuration for the 3DGS model, specifically tailored for indoor modeling scenarios, is determined. Firstly, utilizing a self-collected dataset, a comprehensive comparison was conducted among COLLI-SION-MAPping (abbreviated as COLMAP (V3.7), an open-source software based on Structure from Motion and Multi-View Stereo (SFM-MVS)), Context Capture (V10.2) (abbreviated as CC, a software utilizing oblique photography algorithms), Neural Radiance Fields (NeRF), and the currently renowned 3DGS algorithm. The key dimensions of focus included the number of images, rendering time, and overall rendering effectiveness. Subsequently, based on this comparison, rigorous qualitative and quantitative evaluations are further conducted on the overall performance and detail processing capabilities of the 3DGS algorithm. Finally, to meet the specific requirements of indoor scene modeling and rendering, targeted parameter tuning is performed on the algorithm. The results demonstrate significant performance improvements in the optimized 3DGS algorithm: the PSNR metric increases by 4.3%, and the SSIM metric improves by 0.2%. The experimental results prove that the improved 3DGS algorithm exhibits superior expressive power and persuasiveness in indoor scene rendering. |
| format | Article |
| id | doaj-art-8a5765f87222487382ab837a9c143b4f |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-8a5765f87222487382ab837a9c143b4f2025-01-24T13:35:00ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-01-011412110.3390/ijgi14010021Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and RenderingXinjian Fang0Yingdan Zhang1Hao Tan2Chao Liu3Xu Yang4School of Spatial Information and Surveying and Mapping Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Spatial Information and Surveying and Mapping Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Spatial Information and Surveying and Mapping Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Spatial Information and Surveying and Mapping Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaSchool of Spatial Information and Surveying and Mapping Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaThis study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of various performance indicators under differing resolution conditions, threshold settings for the average magnitude of spatial position gradients, and adjustments to the scaling learning rate, the optimal parameter configuration for the 3DGS model, specifically tailored for indoor modeling scenarios, is determined. Firstly, utilizing a self-collected dataset, a comprehensive comparison was conducted among COLLI-SION-MAPping (abbreviated as COLMAP (V3.7), an open-source software based on Structure from Motion and Multi-View Stereo (SFM-MVS)), Context Capture (V10.2) (abbreviated as CC, a software utilizing oblique photography algorithms), Neural Radiance Fields (NeRF), and the currently renowned 3DGS algorithm. The key dimensions of focus included the number of images, rendering time, and overall rendering effectiveness. Subsequently, based on this comparison, rigorous qualitative and quantitative evaluations are further conducted on the overall performance and detail processing capabilities of the 3DGS algorithm. Finally, to meet the specific requirements of indoor scene modeling and rendering, targeted parameter tuning is performed on the algorithm. The results demonstrate significant performance improvements in the optimized 3DGS algorithm: the PSNR metric increases by 4.3%, and the SSIM metric improves by 0.2%. The experimental results prove that the improved 3DGS algorithm exhibits superior expressive power and persuasiveness in indoor scene rendering.https://www.mdpi.com/2220-9964/14/1/213DGSindoor scene renderingperformance evaluationparameter optimization |
| spellingShingle | Xinjian Fang Yingdan Zhang Hao Tan Chao Liu Xu Yang Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering ISPRS International Journal of Geo-Information 3DGS indoor scene rendering performance evaluation parameter optimization |
| title | Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering |
| title_full | Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering |
| title_fullStr | Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering |
| title_full_unstemmed | Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering |
| title_short | Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering |
| title_sort | performance evaluation and optimization of 3d gaussian splatting in indoor scene generation and rendering |
| topic | 3DGS indoor scene rendering performance evaluation parameter optimization |
| url | https://www.mdpi.com/2220-9964/14/1/21 |
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