面向无位姿激光雷达视图合成的频谱-几何神经场
Spectral-Geometric Neural Fields for Pose-Free LiDAR View Synthesis
把频谱先验作为混合表示来补全稀疏无纹理激光雷达几何，并用特征兼容性置信图做无位姿全局对齐，可直接迁移到无位姿 NeRF/3DGS 的位姿-几何联合优化与稀疏点云重建。
原文依据 a pose-free LiDAR NeRF framework that integrates spectral information with geometric consistency
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论神经辐射场（NeRF）在图像新视角合成（NVS）中取得了显著成功，并启发了向激光雷达新视角合成的扩展。然而，大多数方法严重依赖精确的相机位姿进行场景重建。激光雷达数据的稀疏性与无纹理特性也带来了独特挑战，导致几何空洞与表面不连续。为解决这些问题，我们提出 SG-NLF，一个将频谱信息与几何一致性相结合的无位姿激光雷达 NeRF 框架。具体而言，我们设计了一种基于频谱先验的混合表示以重建平滑几何。在位姿优化方面，我们基于特征兼容性构建置信度感知图以实现全局对齐。此外，引入对抗学习策略以强制跨帧一致性，从而提升重建质量。大量实验证明了该框架的有效性，尤其是在具有挑战性的低频场景中。与先前的最优方法相比，SG-NLF 将重建质量与位姿精度分别提升超过 35.8% 和 68.8%。我们的工作可为激光雷达视图合成提供新的视角。
英文原文摘要 1183 字符
Neural Radiance Fields (NeRF) have shown remarkable success in image novel view synthesis (NVS), inspiring extensions to LiDAR NVS. However, most methods heavily rely on accurate camera poses for scene reconstruction. The sparsity and textureless nature of LiDAR data also present distinct challenges, leading to geometric holes and discontinuous surfaces. To address these issues, we propose SG-NLF, a pose-free LiDAR NeRF framework that integrates spectral information with geometric consistency. Specifically, we design a hybrid representation based on spectral priors to reconstruct smooth geometry. For pose optimization, we construct a confidence-aware graph based on feature compatibility to achieve global alignment. In addition, an adversarial learning strategy is introduced to enforce cross-frame consistency, thereby enhancing reconstruction quality. Comprehensive experiments demonstrate the effectiveness of our framework, especially in challenging low-frequency scenarios. Compared to previous state-of-the-art methods, SG-NLF improves reconstruction quality and pose accuracy by over 35.8% and 68.8%. Our work can provide a novel perspective for LiDAR view synthesis.