同时实现语义分割与高质量激光雷达视角合成，提升自动驾驶感知能力。
SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis
- 分阶段构建平面网格特征，融合多帧全局与单帧局部信息
- 在SemanticKITTI和KITTI-360上实现更优的语义与几何重建效果
- 适合需要真实感激光雷达生成的自动驾驶与机器人场景
近期研究开始探索激光雷达点云的新视角合成（NVS），旨在从未见视角生成逼真的激光雷达扫描。然而，大多数现有方法未能重建语义标签，而这些标签对自动驾驶和机器人感知等下游应用至关重要。与图像可依赖强大分割模型不同，激光雷达点云缺乏大规模预训练模型，导致语义标注耗时且费力。为此，我们提出SN-LiDAR，一种联合实现精准语义分割、高质量几何重建和真实激光雷达合成的方法。具体地，采用从粗到细的平面网格特征表示，从多帧点云中提取全局特征，并利用基于CNN的编码器从当前帧点云中提取局部语义特征。在SemanticKITTI和KITTI-360上的大量实验表明，SN-LiDAR在语义与几何重建方面均表现优越，能有效处理动态物体与大场景。代码将发布于https://github.com/dtc111111/SN-Lidar。
原文摘要 · Abstract (English)
Recent research has begun exploring novel view synthesis (NVS) for LiDAR point clouds, aiming to generate realistic LiDAR scans from unseen viewpoints. However, most existing approaches do not reconstruct semantic labels, which are crucial for many downstream applications such as autonomous driving and robotic perception. Unlike images, which benefit from powerful segmentation models, LiDAR point clouds lack such large-scale pre-trained models, making semantic annotation time-consuming and labor-intensive. To address this challenge, we propose SN-LiDAR, a method that jointly performs accurate semantic segmentation, high-quality geometric reconstruction, and realistic LiDAR synthesis. Specifically, we employ a coarse-to-fine planar-grid feature representation to extract global features from multi-frame point clouds and leverage a CNN-based encoder to extract local semantic features from the current frame point cloud. Extensive experiments on SemanticKITTI and KITTI-360 demonstrate the superiority of SN-LiDAR in both semantic and geometric reconstruction, effectively handling dynamic objects and large-scale scenes. Codes will be available on https://github.com/dtc111111/SN-Lidar.
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