NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

ETH Zurich · Zhejiang University · +3 more institutions

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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…

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Authors

8

Topics & keywords

Keywords
  • Computer science
  • Simultaneous localization and mapping
  • Scalability
  • Artificial intelligence
  • Representation (politics)
  • Computer vision
  • Encoding (memory)
  • Prior probability
UN Sustainable Development Goals
  • Sustainable cities and communities
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