RA-NeRF: Robust Neural Radiance Field Reconstruction with Accurate Camera Pose Estimation under Complex Trajectories

TL;DR

RA-NeRF is a novel method that accurately estimates camera poses and reconstructs scenes using neural radiance fields, even under complex trajectories, by integrating flow-driven pose regulation and implicit pose filtering.

Contribution

It introduces RA-NeRF, which combines photometric consistency, flow-driven pose regulation, and implicit pose filtering to improve camera pose accuracy and scene reconstruction robustness in complex trajectories.

Findings

Achieves state-of-the-art camera pose estimation accuracy.

Demonstrates superior scene reconstruction quality under complex trajectories.

Performs well on challenging datasets like NeRFBuster.

Abstract

Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have emerged as powerful tools for 3D reconstruction and SLAM tasks. However, their performance depends heavily on accurate camera pose priors. Existing approaches attempt to address this issue by introducing external constraints but fall short of achieving satisfactory accuracy, particularly when camera trajectories are complex. In this paper, we propose a novel method, RA-NeRF, capable of predicting highly accurate camera poses even with complex camera trajectories. Following the incremental pipeline, RA-NeRF reconstructs the scene using NeRF with photometric consistency and incorporates flow-driven pose regulation to enhance robustness during initialization and localization. Additionally, RA-NeRF employs an implicit pose filter to capture the camera movement pattern and eliminate the noise for pose estimation. To validate our method, we conduct extensive experiments on the Tanks\&Temple dataset for standard evaluation, as well as the NeRFBuster dataset, which presents challenging camera pose trajectories. On both datasets, RA-NeRF achieves state-of-the-art results in both camera pose estimation and visual quality, demonstrating its effectiveness and robustness in scene reconstruction under complex pose trajectories.