PINGS：高斯泼溅在基于点的隐式神经地图中与距离场相遇
PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map
AI总结：
提出PINGS，一种统一距离场与高斯泼溅辐射场的点基隐式神经地图，用于LiDAR-视觉SLAM，实现高保真重建，提升渲染质量与里程计精度。
摘要：
机器人受益于其环境的高保真重建，这些重建应在几何上准确且具有照片级真实感，以支持下游任务。虽然可以通过从距离传感器构建距离场和从相机构建辐射场来实现这一点，但实现两个场的一致、高质量且可扩展的增量建图具有挑战性。在本文中，我们提出了一种新颖的地图表示，它在弹性且紧凑的基于点的隐式神经地图中统一了连续符号距离场和高斯泼溅辐射场。通过强制这些场之间的几何一致性，我们利用两种模态实现相互改进。我们提出了一种名为PINGS的新型LiDAR-视觉SLAM系统，使用所提出的地图表示，并在多个具有挑战性的大规模数据集上进行了评估。实验结果表明，PINGS可以增量构建全局一致的距离场和辐射场，这些场由一组紧凑的神经点编码。与最先进的方法相比，PINGS通过用距离场约束辐射场，在新视图下实现了优越的光度测量和几何渲染。此外，通过利用辐射场的密集光度线索和多视图一致性，PINGS产生了更准确的距离场，从而改善了里程计估计和网格重建。我们还提供了PINGS的开源实现：https://github.com/PRBonn/PINGS。
英文摘要：
Robots benefit from high-fidelity reconstructions of their environment, which should be geometrically accurate and photorealistic to support downstream tasks. While this can be achieved by building distance fields from range sensors and radiance fields from cameras, realising scalable incremental mapping of both fields consistently and at the same time with high quality is challenging. In this paper, we propose a novel map representation that unifies a continuous signed distance field and a Gaussian splatting radiance field within an elastic and compact point-based implicit neural map. By enforcing geometric consistency between these fields, we achieve mutual improvements by exploiting both modalities. We present a novel LiDAR-visual SLAM system called PINGS using the proposed map representation and evaluate it on several challenging large-scale datasets. Experimental results demonstrate that PINGS can incrementally build globally consistent distance and radiance fields encoded with a compact set of neural points. Compared to state-of-the-art methods, PINGS achieves superior photometric and geometric rendering at novel views by constraining the radiance field with the distance field. Furthermore, by utilizing dense photometric cues and multi-view consistency from the radiance field, PINGS produces more accurate distance fields, leading to improved odometry estimation and mesh reconstruction. We also provide an open-source implementation of PING at: https://github.com/PRBonn/PINGS.
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。
