  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 https://github.com/nubot-nudt/TD-NeRF.
