2021
DOI: 10.48550/arxiv.2112.12130
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NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

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 oversmoothed 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 incorporat… Show more

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Cited by 4 publications
(7 citation statements)
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“…Our work is strongly inspired by iMAP and indeed the active sampling and keyframe selection in iSDF are based on iMAP. NICE-SLAM [39] builds on iMAP by proposing to use a voxel grid of neural fields instead of a single global model. Although not a real-time system, Yan et al [36] investigates offline continual learning for neural fields.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Our work is strongly inspired by iMAP and indeed the active sampling and keyframe selection in iSDF are based on iMAP. NICE-SLAM [39] builds on iMAP by proposing to use a voxel grid of neural fields instead of a single global model. Although not a real-time system, Yan et al [36] investigates offline continual learning for neural fields.…”
Section: Related Workmentioning
confidence: 99%
“…Based on a multi-layer perceptron (MLP) that maps a 3D coordinate to occupancy, these models can be optimised from scratch to accurately fit a specific scene without prior training. Recent work has shown that neural fields can reconstruct highly accurate 3D geometry and that they can be trained in real-time as part of a SLAM system [30,39].…”
Section: Introductionmentioning
confidence: 99%
“…Some works learn a light field for a vivid relighting effect [3,39,65]. In robotics, researchers turn the learning problem inversely to optimize the 6D pose [57] or extend to the environment mapping system [40,67].…”
Section: Novel View Synthesismentioning
confidence: 99%
“…They parameterize viewing rays and points with positional encoding, and need to be re-trained on a scene-by-scene basis. Many recent improvements leverage depth supervision to improve view synthesis in a volume rendering framework [1,5,36,55,62]. An alternative approach replaces volume rendering with a directly learned light field network [44], predicting color values directly from viewing rays.…”
Section: Related Workmentioning
confidence: 99%