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- Title
Real-Time Dense Visual SLAM with Neural Factor Representation.
- Authors
Wei, Weifeng; Wang, Jie; Xie, Xiaolong; Liu, Jie; Su, Pengxiang
- Abstract
Developing a high-quality, real-time, dense visual SLAM system poses a significant challenge in the field of computer vision. NeRF introduces neural implicit representation, marking a notable advancement in visual SLAM research. However, existing neural implicit SLAM methods suffer from long runtimes and face challenges when modeling complex structures in scenes. In this paper, we propose a neural implicit dense visual SLAM method that enables high-quality real-time reconstruction even on a desktop PC. Firstly, we propose a novel neural scene representation, encoding the geometry and appearance information of the scene as a combination of the basis and coefficient factors. This representation allows for efficient memory usage and the accurate modeling of high-frequency detail regions. Secondly, we introduce feature integration rendering to significantly improve rendering speed while maintaining the quality of color rendering. Extensive experiments on synthetic and real-world datasets demonstrate that our method achieves an average improvement of more than 60% for Depth L1 and ATE RMSE compared to existing state-of-the-art methods when running at 9.8 Hz on a desktop PC with a 3.20 GHz Intel Core i9-12900K CPU and a single NVIDIA RTX 3090 GPU. This remarkable advancement highlights the crucial importance of our approach in the field of dense visual SLAM.
- Subjects
COMPUTER vision; VISUAL fields; SPEED; ENCODING; GEOMETRY
- Publication
Electronics (2079-9292), 2024, Vol 13, Issue 16, p3332
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics13163332