The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields
Open accessAbstract
We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from casually taken monocular images. To achieve this, we leverage recent advances in dense monocular SLAM and real-time hierarchical volumetric neural radiance fields. Our insight is that dense monocular SLAM provides the right information to fit a neural radiance field of the scene in real-time, by providing accurate pose estimates and depth-maps with associated uncertainty. Our proposed pipeline achieves better geometric and photometric accuracy than competing approaches (up to 178% better PSNR and 75% better L1 depth), while working in real-time and using only monocular images.
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Citations
Cites
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898 · cited 1×
“VolBA uses a direct RGB loss for pose estimation and mapping, while Orbeez-SLAM and Abou-Chakra et al. use ORB-SLAM [32] to provide the initial poses.”
MethodStructure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_24 · Cited by 5,351
Parallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
Instant neural graphics primitives with a multiresolution hash encoding
Müller, T., Evans, A., Schied, C., & Keller, A. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530127 · Cited by 4,114
(2020) · doi:10.1007/978-3-030-58536-5_24 · Cited by 2,824
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · cited 1×
“For instance, DTAM[10], which achieves dense SLAM, follows the same approach as sparse PTAM[11] by first tracking the camera pose and then estimating the depth in a decoupled manner.”
MethodThe EuRoC micro aerial vehicle datasets
Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M. W., & Siegwart, R. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364915620033 · Cited by 2,066
3D Semantic Parsing of Large-Scale Indoor Spaces
Armeni, I., Sener, O., Zamir, A. R., Jiang, H., Brilakis, I., Fischer, M., & Savarese, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.170 · Cited by 1,892
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Tancik, M., Srinivasan, P. P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J. T., & Ng, R. (2020) · arXiv · doi:10.48550/arxiv.2006.10739 · Cited by 1,459
Metadata
- Authors
- Rosinol, A., Leonard, J. J., & Carlone, L.
- Venue
- 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2023-10-01
- Pages
- 3437-3444
- OpenAlex
- W4389665446
- Cited by
- 281
- References
- 34
- FWCI
- 154.84
- Type
- conference paper
- Language
- en
- Open access
- green
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- Rosinol, A., Leonard, J. J., & Carlone, L. (2023). NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros55552.2023.10341922
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