NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
(2022) 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 p.12786-12796- Abstract
- Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over- smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. In this paper, we present NICE-SLAM, a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation. Optimizing this representation with pre-trained geometric priors enables detailed reconstruction on large indoor scenes. Compared to recent neural... (More)
- Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over- smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. In this paper, we present NICE-SLAM, a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation. Optimizing this representation with pre-trained geometric priors enables detailed reconstruction on large indoor scenes. Compared to recent neural implicit SLAM systems, our approach is more scalable, efficient, and robust. Experiments on five challenging datasets demonstrate competitive results of NICE-SLAM in both mapping and tracking quality. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1a9abc5b-5328-40e1-9b08-4d990f417366
- author
- Zhu, Zihan ; Peng, Songyou ; Larsson, Viktor LU ; Xu, Weiwei ; Bao, Hujun ; Cui, Zhaopeng ; Oswald, Martin R and Pollefeys, Marc
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
- pages
- 11 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
- conference location
- New Orleans, United States
- conference dates
- 2022-06-19 - 2022-06-24
- external identifiers
-
- scopus:85141811894
- ISBN
- 978-1-6654-6946-3
- 978-1-6654-6947-0
- DOI
- 10.1109/CVPR52688.2022.01245
- language
- English
- LU publication?
- yes
- id
- 1a9abc5b-5328-40e1-9b08-4d990f417366
- date added to LUP
- 2022-09-06 13:24:25
- date last changed
- 2025-03-09 01:15:40
@inproceedings{1a9abc5b-5328-40e1-9b08-4d990f417366, abstract = {{Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over- smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. In this paper, we present NICE-SLAM, a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation. Optimizing this representation with pre-trained geometric priors enables detailed reconstruction on large indoor scenes. Compared to recent neural implicit SLAM systems, our approach is more scalable, efficient, and robust. Experiments on five challenging datasets demonstrate competitive results of NICE-SLAM in both mapping and tracking quality.}}, author = {{Zhu, Zihan and Peng, Songyou and Larsson, Viktor and Xu, Weiwei and Bao, Hujun and Cui, Zhaopeng and Oswald, Martin R and Pollefeys, Marc}}, booktitle = {{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}}, isbn = {{978-1-6654-6946-3}}, language = {{eng}}, pages = {{12786--12796}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{NICE-SLAM: Neural Implicit Scalable Encoding for SLAM}}, url = {{http://dx.doi.org/10.1109/CVPR52688.2022.01245}}, doi = {{10.1109/CVPR52688.2022.01245}}, year = {{2022}}, }