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NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

📅 June 1, 2022 👤 Zihan Zhu, Songyou Peng, Viktor Larsson et al. 📖 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 📊 734 citations

🤖 Plain-English Summary

Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Experiments on five challenging datasets demonstrate competitive results of NICE-SLAM in both mapping and tracking quality.

🔑 Key Findings

  • 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.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Jun 01, 2022
Journal 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Authors Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao
DOI 10.1109/cvpr52688.2022.01245
Citations 734
Source OpenAlex

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