NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
ETH Zurich · Zhejiang University · +3 more institutions
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…
Citation impact
- FWCI
- 227.42
- Percentile
- 100%
- References
- 89
Authors
8Topics & keywords
- Computer science
- Simultaneous localization and mapping
- Scalability
- Artificial intelligence
- Representation (politics)
- Computer vision
- Encoding (memory)
- Prior probability
- Sustainable cities and communities