针对激光雷达神经辐射场，提出高效联合优化点云与位姿的算法
Neural LiDAR Bundle Adjustment
- 基于激光雷达采样密度关键性，设计专用体积采样策略
- 在两个数据集上实现当前最优多视角点云配准效果
- 适合做激光雷达3D重建与自动驾驶定位的研究者参考
近期研究在神经辐射场（NeRF）基础上实现了出色的视图合成与场景重建效果，包括扩展至激光雷达模态。然而，很少有工作探讨RGB NeRF与激光雷达NeRF之间的关键设计差异，尤其未考虑其底层原理。本文通过理论和实证分析表明，体素采样密度在激光雷达NeRF中起决定性作用。基于此发现，提出一种新型神经激光雷达束调整（NeLD-BA）算法，采用高效的激光雷达射线体素采样方法，实现激光雷达地图与位姿的联合优化。在Newer College和FusionPortable数据集上进行了大量实验，验证了该方法在多视角点云配准与三维建图任务中的先进性能。代码将开源以供社区使用。
原文摘要 · Abstract (English)
Recent research has achieved remarkable novel view rendering and scene reconstruction results with Neural Radiance Field (NeRF), including extensions to the LiDAR modality. Few studies have, however, explored the key design differences between RGB NeRFs and LiDAR NeRFs, particularly considering their underlying working principles. In this work, we provide both theoretical and empirical evidence suggesting that the density of volume sampling plays a significant role in LiDAR NeRF. Based on this finding, we propose a novel Neural LiDAR Bundle Adjustment (NeLD-BA) algorithm, which is tailored using efficient volume sampling of LiDAR rays for joint optimization of LiDAR map and poses. Extensive experiments are performed using the Newer College and FusionPortable datasets to demonstrate the proposed NeLD-BA's state-of-the-art performance in multi-view point cloud registration and 3D mapping. We will open-source our code for the community.
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