AI RESEARCH
Spectral-Geometric Neural Fields for Pose-Free LiDAR View Synthesis
arXiv CS.CV
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ArXi:2603.12903v1 Announce Type: new 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.