无需精确位姿即可高质量合成LiDAR视角，解决稀疏无纹理数据的几何缺陷。
Spectral-Geometric Neural Fields for Pose-Free LiDAR View Synthesis
- 融合光谱先验与几何一致性构建混合表征，重建平滑场景结构。
- 通过特征兼容性构建置信度感知图，实现全局位姿对齐，提升精度68.8%。
- 采用对抗学习增强跨帧一致性，适合低频复杂场景下的自动驾驶应用。
神经辐射场（NeRF）在图像新视角合成中表现卓越，推动了LiDAR新视角合成的发展。然而，现有方法严重依赖精确相机位姿进行场景重建。LiDAR数据的稀疏性和无纹理特性带来几何空洞和表面不连续问题。为此，本文提出SG-NLF——一种无需位姿的LiDAR NeRF框架，结合光谱信息与几何一致性。我们设计基于光谱先验的混合表征以重建平滑几何；针对位姿优化，构建基于特征兼容性的置信度感知图实现全局对齐；同时引入对抗学习策略，强化跨帧一致性，从而提升重建质量。大量实验表明，该框架在低频挑战场景下表现优异，相比先前最优方法，重建质量提升超35.8%，位姿精度提升68.8%。本工作为LiDAR视角合成提供了新思路。
原文摘要 · Abstract (English)
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.
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