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Real-Time Dense Visual SLAM with Neural Factor Representation.
- Source :
- Electronics (2079-9292); Aug2024, Vol. 13 Issue 16, p3332, 17p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- COMPUTER vision
VISUAL fields
SPEED
ENCODING
GEOMETRY
Subjects
Details
- Language :
- English
- ISSN :
- 20799292
- Volume :
- 13
- Issue :
- 16
- Database :
- Complementary Index
- Journal :
- Electronics (2079-9292)
- Publication Type :
- Academic Journal
- Accession number :
- 179383101
- Full Text :
- https://doi.org/10.3390/electronics13163332