用场景坐标回归实现实时神经隐式建图，速度快且隐私保护好。
ACE-SLAM: Scene Coordinate Regression for Neural Implicit Real-Time SLAM
- 用轻量网络直接将图像特征映射为3D全局坐标，构建隐式地图。
- 首次在神经隐式SLAM中实现严格实时运行，重定位速度极快。
- 适合需要低延迟和隐私保护的实时机器人导航应用。
我们提出一种新型神经RGB-D同步定位与建图（SLAM）系统，可在实时条件下学习场景的隐式地图。首次将场景坐标回归（SCR）作为神经SLAM流水线的核心隐式表示，通过训练轻量级网络，直接将2D图像特征映射至3D全局坐标。SCR网络提供高效、低内存的3D地图表示，支持极快的重定位，并天然具备隐私保护特性，特别适用于神经隐式SLAM。我们的系统是首个在神经隐式RGB-D SLAM中实现严格实时运行的方案，依赖于基于SCR的表示。我们设计了一种专为此目的定制的SCR架构，并详细阐述了将SCR集成到实时SLAM流水线所需的关键设计选择。所提出的框架简单而灵活，可无缝支持稀疏与密集特征，在动态环境中无需特殊调整即可稳定运行。我们在已有的合成与真实世界基准上评估该方法，结果表明其性能媲美当前最优水平。
原文摘要 · Abstract (English)
We present a novel neural RGB-D Simultaneous Localization And Mapping (SLAM) system that learns an implicit map of the scene in real time. For the first time, we explore the use of Scene Coordinate Regression (SCR) as the core implicit map representation in a neural SLAM pipeline, a paradigm that trains a lightweight network to directly map 2D image features to 3D global coordinates. SCR networks provide efficient, low-memory 3D map representations, enable extremely fast relocalization, and inherently preserve privacy, making them particularly suitable for neural implicit SLAM. Our system is the first one to achieve strict real-time in neural implicit RGB-D SLAM by relying on a SCR-based representation. We introduce a novel SCR architecture specifically tailored for this purpose and detail the critical design choices required to integrate SCR into a live SLAM pipeline. The resulting framework is simple yet flexible, seamlessly supporting both sparse and dense features, and operates reliably in dynamic environments without special adaptation. We evaluate our approach on established synthetic and real-world benchmarks, demonstrating competitive performance against the state of the art. Project Page: https://github.com/ialzugaray/ace-slam
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