The reliance on accurate camera poses is a significant barrier to the
widespread deployment of Neural Radiance Fields (NeRF) models for 3D
reconstruction and SLAM tasks. The existing method introduces monocular depth
priors to jointly optimize the camera poses and NeRF, which fails to fully
exploit the depth priors and neglects the impact of their inherent noise. In
this paper, we propose Truncated Depth NeRF (TD-NeRF), a novel approach that
enables training NeRF from unknown camera poses - by jointly optimizing
learnable parameters of the radiance field and camera poses. Our approach
explicitly utilizes monocular depth priors through three key advancements: 1)
we propose a novel depth-based ray sampling strategy based on the truncated
normal distribution, which improves the convergence speed and accuracy of pose
estimation; 2) to circumvent local minima and refine depth geometry, we
introduce a coarse-to-fine training strategy that progressively improves the
depth precision; 3) we propose a more robust inter-frame point constraint that
enhances robustness against depth noise during training. The experimental
results on three datasets demonstrate that TD-NeRF achieves superior
performance in the joint optimization of camera pose and NeRF, surpassing prior
works, and generates more accurate depth geometry. The implementation of our
method has been released at this https URL

通过联合优化辐射场和相机姿态，利用截断深度先验实现了从未知相机姿态训练 NeRF 的新方法 TD-NeRF，通过三个关键进展：1）提出基于截断正态分布的新型基于深度的射线采样策略，改善了姿态估计的收敛速度和准确性；2）引入了逐步改进深度精确度的粗到精的训练策略，以克服局部最小值和改善深度几何；3）提出了更强鲁棒的帧间点约束，在训练期间增强了对深度噪声的鲁棒性。在三个数据集上的实验结果表明，TD-NeRF 在相机姿态和 NeRF 的联合优化中取得了优越的性能，超过了先前的工作，并生成了更准确的深度几何。