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 [BibTeX] [Marc21]
ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields
Type of publication: Conference paper
Citation: Johari_CVPR_2023
Publication status: Published
Booktitle: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023
Pages: 17408-17419
ISSN: 1063-6919
ISBN: 979-8-3503-0129-8
DOI: https://doi.org/10.1109/CVPR52729.2023.01670
Abstract: We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM system, resulting in an efficient and accurate dense visual SLAM method. Our scene representation consists of multiscale axis-aligned perpendicular feature planes and shallow decoders that, for each point in the continuous space, decode the interpolated features into Truncated Signed Distance Field (TSDF) and RGB values. Our extensive experiments on three standard datasets, Replica, ScanNet, and TUM RGB-D show that ESLAM improves the accuracy of 3D reconstruction and camera localization of state-of-the-art dense visual SLAM methods by more than 50%, while it runs up to x10 faster and does not require any pre-training. Project page: https://www.idiap.ch/paper/eslam
Projects Idiap
Authors Johari, Mohammad Mahdi
Carta, Camilla
Fleuret, Francois
Added by: [UNK]
Total mark: 0
  • Johari_CVPR_2023.pdf