Implicit neural representation has emerged as a powerful method for
reconstructing 3D scenes from 2D images. Given a set of camera poses and
associated images, the models can be trained to synthesize novel, unseen views.
In order to expand the use cases for implicit neural representations, we need
to incorporate camera pose estimation capabilities as part of the
representation learning, as this is necessary for reconstructing scenes from
real-world video sequences where cameras are generally not being tracked.
Existing approaches like COLMAP and, most recently, bundle-adjusting neural
radiance field methods often suffer from lengthy processing times. These delays
ranging from hours to days, arise from laborious feature matching, hardware
limitations, dense point sampling, and long training times required by a
multi-layer perceptron structure with a large number of parameters. To address
these challenges, we propose a framework called bundle-adjusting accelerated
neural graphics primitives (BAA-NGP). Our approach leverages accelerated
sampling and hash encoding to expedite both pose refinement/estimation and 3D
scene reconstruction. Experimental results demonstrate that our method achieves
a more than 10 to 20 $\times$ speed improvement in novel view synthesis
compared to other bundle-adjusting neural radiance field methods without
sacrificing the quality of pose estimation.

本文提出了一种基于加速采样和哈希编码的框架 BAA-NGP，它能够加快位姿估计、场景重建和新视角合成的处理速度，并在不降低位姿估计质量的情况下，比其他基于束调整的神经辐射场方法提高了十倍至二十倍的速度。