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Benefiting from the remarkable performance of Neural Radiance Fields (NeRF) technology in 3D reconstruction, its integration into Simultaneous Localization and Mapping (SLAM) tasks for map representation has become a widely recognized and novel approach in recent years. This paper proposes an improved end-to-end multilevel NeRF-based dense RGB-D SLAM, building upon the current state-of-the-art NICE-SLAM. Firstly, we enhance the structure design of the multilevel MLP, improving its ability to represent high-level details and enhancing network scalability. Secondly, we refine the keyframe selection strategy to alleviate network forgetting issues. Finally, improvements are made in eliminating depth uncertainty and refining the multi-level weight settings to further enhance system performance. Extensive experiments across multiple datasets validate the accuracy improvement of our method compared to NICE-SLAM.

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Improved End-to-End Multilevel NeRF-Based Dense RGB-D SLAM

  • 会议论文

  • 发表日期：

    2025年

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  • Haojun Zhang,
  • Yuan Yao,
  • Xuefeng Yan

摘要

Benefiting from the remarkable performance of Neural Radiance Fields (NeRF) technology in 3D reconstruction, its integration into Simultaneous Localization and Mapping (SLAM) tasks for map representation has become a widely recognized and novel approach in recent years. This paper proposes an improved end-to-end multilevel NeRF-based dense RGB-D SLAM, building upon the current state-of-the-art NICE-SLAM. Firstly, we enhance the structure design of the multilevel MLP, improving its ability to represent high-level details and enhancing network scalability. Secondly, we refine the keyframe selection strategy to alleviate network forgetting issues. Finally, improvements are made in eliminating depth uncertainty and refining the multi-level weight settings to further enhance system performance. Extensive experiments across multiple datasets validate the accuracy improvement of our method compared to NICE-SLAM.