用多层级高斯点云实现高精度彩色、深度与语义重建
RGBDS-SLAM: A RGB-D Semantic Dense SLAM Based on 3D Multi Level Pyramid Gaussian Splatting
- 基于多层级图像金字塔优化高斯点云训练，提升细节还原能力
- 在Replica和ScanNet上优于现有方法，语义与几何一致性显著增强
- 适合需要高保真三维场景重建的研究者与开发者
高质量重建对密集式同步定位与地图构建（SLAM）至关重要。近期主流方法采用3D高斯点云（3D GS）技术实现场景的彩色、深度与语义重建。然而，这些方法常忽略场景不同区域在细节与一致性上的问题。为此，我们提出RGBDS-SLAM，一种基于3D多层级金字塔高斯点云的RGB-D语义密集SLAM系统，实现彩色、深度与语义的高质量密集重建。该系统引入3D多层级金字塔高斯点云方法，通过提取多层级图像金字塔进行高斯点云训练，恢复场景细节并保证彩色、深度与语义重建的一致性。此外，设计紧耦合的多特征重建优化机制，在渲染优化过程中使彩色、深度与语义图相互增强。在Replica和ScanNet公开数据集上的大量定量、定性和消融实验表明，所提方法优于当前最先进方法。开源代码将发布于：https://github.com/zhenzhongcao/RGBDS-SLAM。
原文摘要 · Abstract (English)
High-quality reconstruction is crucial for dense SLAM. Recent popular approaches utilize 3D Gaussian Splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. However, these methods often overlook issues of detail and consistency in different parts of the scene. To address this, we propose RGBDS-SLAM, a RGB-D semantic dense SLAM system based on 3D multi-level pyramid gaussian splatting, which enables high-quality dense reconstruction of scene RGB, depth, and semantics.In this system, we introduce a 3D multi-level pyramid gaussian splatting method that restores scene details by extracting multi-level image pyramids for gaussian splatting training, ensuring consistency in RGB, depth, and semantic reconstructions. Additionally, we design a tightly-coupled multi-features reconstruction optimization mechanism, allowing the reconstruction accuracy of RGB, depth, and semantic maps to mutually enhance each other during the rendering optimization process. Extensive quantitative, qualitative, and ablation experiments on the Replica and ScanNet public datasets demonstrate that our proposed method outperforms current state-of-the-art methods. The open-source code will be available at: https://github.com/zhenzhongcao/RGBDS-SLAM.
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