CVPR 2023 - Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields

In this episode we discuss Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields by Author 1: Yue Chen Affiliation: Xi’an Jiaotong University Author 2: Xingyu Chen Affiliation: Xi’an Jiaotong University Author 3: Xuan Wang Affiliation: Ant Group Author 4: Qi Zhang Affiliation: Tencent AI Lab Author 5: Yu Guo Affiliation: Xi’an Jiaotong University Author 6: Ying Shan Affiliation: Tencent AI Lab Author 7: Fei Wang Affiliation: Xi’an Jiaotong University. The paper proposes a method called L2G-NeRF for bundle-adjusting Neural Radiance Fields (NeRF). NeRF has achieved realistic synthesis of novel views but is limited by the requirement of accurate camera poses. L2G-NeRF performs pixel-wise flexible alignment followed by frame-wise constrained parametric alignment to improve high-fidelity reconstruction and resolve large camera pose misalignment. The method outperforms current state-of-the-art and is an easy-to-use plugin that can be applied to NeRF variants and other neural field applications.