TY - GEN
T1 - RA-NeRF
T2 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2025
AU - Yan, Qingsong
AU - Wang, Qiang
AU - Zhao, Kaiyong
AU - Chen, Jie
AU - Li, Bo
AU - Chu, Xiaowen
AU - Deng, Fei
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105029964452
U2 - 10.1109/IROS60139.2025.11246397
DO - 10.1109/IROS60139.2025.11246397
M3 - 会议稿件
AN - SCOPUS:105029964452
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 13133
EP - 13140
BT - IROS 2025 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, Conference Proceedings
A2 - Laugier, Christian
A2 - Renzaglia, Alessandro
A2 - Atanasov, Nikolay
A2 - Birchfield, Stan
A2 - Cielniak, Grzegorz
A2 - De Mattos, Leonardo
A2 - Fiorini, Laura
A2 - Giguere, Philippe
A2 - Hashimoto, Kenji
A2 - Ibanez-Guzman, Javier
A2 - Kamegawa, Tetsushi
A2 - Lee, Jinoh
A2 - Loianno, Giuseppe
A2 - Luck, Kevin
A2 - Maruyama, Hisataka
A2 - Martinet, Philippe
A2 - Moradi, Hadi
A2 - Nunes, Urbano
A2 - Pettre, Julien
A2 - Pretto, Alberto
A2 - Ranzani, Tommaso
A2 - Ronnau, Arne
A2 - Rossi, Silvia
A2 - Rouse, Elliott
A2 - Ruggiero, Fabio
A2 - Simonin, Olivier
A2 - Wang, Danwei
A2 - Yang, Ming
A2 - Yoshida, Eiichi
A2 - Zhao, Huijing
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 October 2025 through 25 October 2025
ER -