将端到端追踪与3D高斯点云渲染结合，提升单目SLAM的精度与速度。
DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting
- 端到端追踪+3D高斯点云渲染，实现高效建图与渲染
- 在常见SLAM基准上达到跟踪与渲染的顶尖水平
- 支持消费级显卡实时运行，无需已知相机参数
近期场景合成进展使得仅通过优化超原始体并以渲染为目标的独立式SLAM系统成为可能。然而，其追踪性能仍落后于传统及端到端SLAM系统。在鲁棒性、速度与精度之间尚未达成最优平衡，尤其在单目视频场景下。本文提出基于端到端追踪器并融合最新3D高斯点云渲染技术的SLAM系统——DroidSplat。该框架在常见SLAM基准上实现了跟踪与渲染的最先进（SotA）表现。我们并行实现现代SLAM系统的多个构建模块，可在普通消费级GPU上实现快速推理。得益于近期单目深度预测与相机标定的进步，系统即使在无已知相机内参的真实数据上也能取得优异结果。代码将公开于https://github.com/ChenHoy/DROID-Splat。
原文摘要 · Abstract (English)
Recent progress in scene synthesis makes standalone SLAM systems purely based on optimizing hyperprimitives with a Rendering objective possible. However, the tracking performance still lacks behind traditional and end-to-end SLAM systems. An optimal trade-off between robustness, speed and accuracy has not yet been reached, especially for monocular video. In this paper, we introduce a SLAM system based on an end-to-end Tracker and extend it with a Renderer based on recent 3D Gaussian Splatting techniques. Our framework \textbf{DroidSplat} achieves both SotA tracking and rendering results on common SLAM benchmarks. We implemented multiple building blocks of modern SLAM systems to run in parallel, allowing for fast inference on common consumer GPU's. Recent progress in monocular depth prediction and camera calibration allows our system to achieve strong results even on in-the-wild data without known camera intrinsics. Code will be available at \url{https://github.com/ChenHoy/DROID-Splat}.
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