发表机构
Technical University of Munich; Munich Center for Machine Learning（慕尼黑工业大学; 慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Stipple方法，结合Basalt视觉-惯性跟踪系统与Brush增量3D高斯溅射技术，实现实时同步跟踪重建，替代3DGS繁重步骤，为机器人与XR的实时3D重建提供解决方案。
AI 中文摘要
3D高斯溅射（3DGS）可高效渲染照片级真实感场景，但其繁重的预处理与训练步骤使其难以适配机器人或扩展现实（XR）领域需要实时重建的应用，而这类应用需要对新环境提供即时反馈与交互。视觉-惯性里程计（VIO）与同步定位与建图（VI-SLAM）系统专门针对这类实时应用，适合与3DGS集成。本文提出一种新方法，通过利用基于Basalt的高效视觉-惯性跟踪系统，结合基于Brush（一款高效的、基于Rust的、与GPU厂商无关的3D高斯溅射实现）构建的新型增量方法，实现实时同步跟踪与重建。研究表明，3DGS的诸多繁重预处理与训练步骤可被更高效的增量训练策略替代，该策略可直接访问视觉-惯性跟踪系统生成的信息；此外，本文还提出并结合了多项实用改进，以提升训练流水线效率，使其可与跟踪线程并行实时运行。本研究凸显了挖掘SLAM与3DGS互补性的价值，及其在实时3D重建领域的应用潜力。
英文摘要
3D Gaussian Splatting (3DGS) provides efficient rendering of photo-realistic scenes, but its heavy preprocessing and training steps make it a poor fit for applications that require real-time reconstruction in robotics or XR. This capability is important since it allows immediate feedback and interaction with new environments. Visual-inertial odometry (VIO) and simultaneous localization and mapping (VI-SLAM) systems, on the other hand, specifically target these real-time applications, which makes them a good choice for integration with 3DGS. We propose a new method that tracks and reconstructs simultaneously in real-time by leveraging an efficient visual-inertial tracking system based on Basalt together with a novel incremental method built on top of Brush, an efficient Rust-based GPU-vendor-agnostic implementation of 3D Gaussian Splatting. We show that many of the heavy preprocessing and training steps of 3DGS can be replaced with a more efficient incremental training strategy that has direct access to the information generated by the visual-inertial tracking system. Furthermore, we propose and combine multiple practical improvements to increase the efficiency of the training pipeline and adapt it to run in real-time, parallel to the tracking thread. This work highlights the value of exploiting the complementary nature of SLAM and 3DGS, and how that can lead to promising results for real-time 3D reconstruction.
