ES-Gaussian: Gaussian Splatting Mapping via Error Space-Based Gaussian Completion
| Autor: | Chen, Lu, Zeng, Yingfu, Li, Haoang, Deng, Zhitao, Yan, Jiafu, Zhao, Zhenjun |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | Accurate and affordable indoor 3D reconstruction is critical for effective robot navigation and interaction. Traditional LiDAR-based mapping provides high precision but is costly, heavy, and power-intensive, with limited ability for novel view rendering. Vision-based mapping, while cost-effective and capable of capturing visual data, often struggles with high-quality 3D reconstruction due to sparse point clouds. We propose ES-Gaussian, an end-to-end system using a low-altitude camera and single-line LiDAR for high-quality 3D indoor reconstruction. Our system features Visual Error Construction (VEC) to enhance sparse point clouds by identifying and correcting areas with insufficient geometric detail from 2D error maps. Additionally, we introduce a novel 3DGS initialization method guided by single-line LiDAR, overcoming the limitations of traditional multi-view setups and enabling effective reconstruction in resource-constrained environments. Extensive experimental results on our new Dreame-SR dataset and a publicly available dataset demonstrate that ES-Gaussian outperforms existing methods, particularly in challenging scenarios. The project page is available at https://chenlu-china.github.io/ES-Gaussian/. Comment: This preprint has been withdrawn due to concerns regarding the originality of certain technical elements, as well as its basis in a company project report that was intended solely for internal discussions. To avoid any potential misunderstandings, we have decided to withdraw this submission from public access. We apologize for any confusion this may have caused |
| Databáze: | arXiv |
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