  Due to the limited model capacity, leveraging distributed Neural Radiance
Fields (NeRFs) for modeling extensive urban environments has become a
necessity. However, current distributed NeRF registration approaches encounter
aliasing artifacts, arising from discrepancies in rendering resolutions and
suboptimal pose precision. These factors collectively deteriorate the fidelity
of pose estimation within NeRF frameworks, resulting in occlusion artifacts
during the NeRF blending stage. In this paper, we present a distributed NeRF
system with tri-stage pose optimization. In the first stage, precise poses of
images are achieved by bundle adjusting Mip-NeRF 360 with a coarse-to-fine
strategy. In the second stage, we incorporate the inverting Mip-NeRF 360,
coupled with the truncated dynamic low-pass filter, to enable the achievement
of robust and precise poses, termed Frame2Model optimization. On top of this,
we obtain a coarse transformation between NeRFs in different coordinate
systems. In the third stage, we fine-tune the transformation between NeRFs by
Model2Model pose optimization. After obtaining precise transformation
parameters, we proceed to implement NeRF blending, showcasing superior
performance metrics in both real-world and simulation scenarios. Codes and data
will be publicly available at https://github.com/boilcy/Distributed-NeRF.
