RA-NeRF在复杂运动轨迹下实现高精度相机位姿估计，提升3D重建质量。
RA-NeRF: Robust Neural Radiance Field Reconstruction with Accurate Camera Pose Estimation under Complex Trajectories
- 基于光度一致性与流驱动位姿调节，逐步优化重建过程。
- 在Tanks&Temple和NeRFBuster数据集上均达到最优位姿精度与视觉效果。
- 适合需要高鲁棒性3D重建的复杂运动场景应用。
神经辐射场（NeRF）和3D高斯泼溅（3DGS）已成为3D重建与SLAM任务的强大工具，但其性能高度依赖准确的相机位姿先验。现有方法虽引入外部约束，但在复杂相机轨迹下仍难以达到满意精度。本文提出RA-NeRF，可在复杂轨迹下实现高精度相机位姿预测。该方法采用增量式流程，利用NeRF进行场景重建，并通过光度一致性保证几何一致性，结合流驱动的位姿调节提升初始化与定位阶段的鲁棒性。此外，引入隐式位姿滤波器捕捉相机运动模式，有效消除位姿估计中的噪声。为验证方法有效性，在Tanks&Temple标准数据集及包含挑战性位姿轨迹的NeRFBuster数据集上进行大量实验。结果表明，RA-NeRF在相机位姿估计与视觉质量方面均达到当前最优水平，充分证明其在复杂位姿轨迹下的有效性与鲁棒性。
原文摘要 · Abstract (English)
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.
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