Neural radiance field (NeRF) has achieved impressive results in high-quality
3D scene reconstruction. However, NeRF heavily relies on precise camera poses.
While recent works like BARF have introduced camera pose optimization within
NeRF, their applicability is limited to simple trajectory scenes. Existing
methods struggle while tackling complex trajectories involving large rotations.
To address this limitation, we propose CT-NeRF, an incremental reconstruction
optimization pipeline using only RGB images without pose and depth input. In
this pipeline, we first propose a local-global bundle adjustment under a pose
graph connecting neighboring frames to enforce the consistency between poses to
escape the local minima caused by only pose consistency with the scene
structure. Further, we instantiate the consistency between poses as a
reprojected geometric image distance constraint resulting from pixel-level
correspondences between input image pairs. Through the incremental
reconstruction, CT-NeRF enables the recovery of both camera poses and scene
structure and is capable of handling scenes with complex trajectories. We
evaluate the performance of CT-NeRF on two real-world datasets, NeRFBuster and
Free-Dataset, which feature complex trajectories. Results show CT-NeRF
outperforms existing methods in novel view synthesis and pose estimation
accuracy.

CT-NeRF 是一种仅使用 RGB 图像而不需要姿态和深度信息的增量重建优化管道，通过本地 - 全局捆绑调整和重投影几何图像距离约束，实现对相机姿态和场景结构的恢复，能够处理具有复杂轨迹的场景，并在新视角合成和姿态估计准确度方面优于现有方法。