Bi-Resolution Hash Encoding in Neural Radiance Fields: A Method for Accelerated Pose Optimization and Enhanced Reconstruction Efficiency

Abstract

NeRF has garnered extensive attention from researchers due to its impressive performance in three-dimensional scene reconstruction and realistic rendering. It is perceived as a potential pivotal technology for scene reconstruction in fields such as virtual reality and augmented reality. However, most NeRF-related research and applications heavily rely on precise pose data. The challenge of effectively reconstructing scenes in situations with inaccurate or missing pose data remains pressing. To address this issue, we examine the relationship between different resolution encodings and pose estimation and introduce BiResNeRF, a scene reconstruction method based on both low and high-resolution hash encoding modules, accompanied by a two-stage training strategy. The training strategy includes setting different learning rates and sampling strategies for different stages, designing stage transition signals, and implementing a smooth warm-up learning rate scheduling strategy after the phase transition. The experimental results indicate that our method not only ensures high synthesis quality but also reduces training time. Compared to other algorithms that jointly optimize pose, our training process is sped up by at least 1.3×. In conclusion, our approach efficiently reconstructs scenes under inaccurate poses and offers fresh perspectives and methodologies for pose optimization research in NeRF.