Camera-Lidar Consistent Neural Radiance Fields

Abstract

Neural Radiance Fields (NeRFs) have become a leading technique for novel view synthesis, with promising applications in robotics. However, due to shape-radiance ambiguity, NeRFs often require additional depth inputs for regularization in outdoor scenarios. LiDAR provides accurate depth measurements, but current methods typically combine only a few frames, resulting in sparse depth maps and discrepancies with camera images. The asynchronous nature of LiDAR, where each point is captured at a different timestamp, introduces depth inaccuracies when treated as simultaneous. These errors, along with inherent LiDAR noise, create inconsistencies that hinder reconstruction accuracy. To address these challenges, we propose a continuous-time framework for joint Camera-LiDAR optimization, enabling more consistent radiance field reconstruction and improving both view synthesis and geometric accuracy. To address these issues, we introduce a continuoustime framework for joint Camera-LiDAR optimization, aiming to consistently reconstruct the radiance field for better view synthesis and geometric accuracy.