Spectral-Geometric Neural Fields for Pose-Free LiDAR View Synthesis
This work addresses pose-free LiDAR view synthesis for applications like autonomous driving or robotics, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the problem of LiDAR novel view synthesis without requiring accurate camera poses, addressing challenges like sparsity and textureless data that cause geometric holes. The proposed SG-NLF framework improves reconstruction quality by 35.8% and pose accuracy by 68.8% compared to previous state-of-the-art methods.
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.