  Multi-resolution hash encoding has recently been proposed to reduce the
computational cost of neural renderings, such as NeRF. This method requires
accurate camera poses for the neural renderings of given scenes. However,
contrary to previous methods jointly optimizing camera poses and 3D scenes, the
naive gradient-based camera pose refinement method using multi-resolution hash
encoding severely deteriorates performance. We propose a joint optimization
algorithm to calibrate the camera pose and learn a geometric representation
using efficient multi-resolution hash encoding. Showing that the oscillating
gradient flows of hash encoding interfere with the registration of camera
poses, our method addresses the issue by utilizing smooth interpolation
weighting to stabilize the gradient oscillation for the ray samplings across
hash grids. Moreover, the curriculum training procedure helps to learn the
level-wise hash encoding, further increasing the pose refinement. Experiments
on the novel-view synthesis datasets validate that our learning frameworks
achieve state-of-the-art performance and rapid convergence of neural rendering,
even when initial camera poses are unknown.
