DROID-Splat：端到端SLAM与3D高斯溅射的结合
DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting
AI总结：
针对单目视频SLAM在鲁棒性、速度和准确性间平衡不足的问题，提出结合端到端跟踪器与3D高斯溅射渲染器的DroidSplat框架，实现了基准测试中最先进的跟踪和渲染结果，且支持未知内参的野外数据。
摘要：
场景合成的最新进展使得纯粹基于渲染目标优化超基元的独立SLAM系统成为可能。然而，其跟踪性能仍落后于传统和端到端SLAM系统。在鲁棒性、速度和准确性之间尚未达到最佳平衡，尤其是对于单目视频。本文介绍了一种基于端到端跟踪器的SLAM系统，并利用最新的3D高斯溅射技术扩展了渲染器。我们的框架DroidSplat在常见SLAM基准测试中实现了最先进的跟踪和渲染结果。我们实现了现代SLAM系统的多个构建块以并行运行，从而在普通消费级GPU上实现快速推理。单目深度预测和相机校准的最新进展使我们的系统即使在未知相机内参的野外数据上也能取得优异结果。代码将在https://github.com/ChenHoy/DROID-Splat提供。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
