Neural Radiance Fields (NeRFs) have shown remarkable success in synthesizing
photorealistic views from multi-view images of static scenes, but face
challenges in dynamic, real-world environments with distractors like moving
objects, shadows, and lighting changes. Existing methods manage controlled
environments and low occlusion ratios but fall short in render quality,
especially under high occlusion scenarios. In this paper, we introduce NeRF
On-the-go, a simple yet effective approach that enables the robust synthesis of
novel views in complex, in-the-wild scenes from only casually captured image
sequences. Delving into uncertainty, our method not only efficiently eliminates
distractors, even when they are predominant in captures, but also achieves a
notably faster convergence speed. Through comprehensive experiments on various
scenes, our method demonstrates a significant improvement over state-of-the-art
techniques. This advancement opens new avenues for NeRF in diverse and dynamic
real-world applications.

通过简单高效的方法，在复杂的野外场景中从仅仅随意捕捉的图像序列中合成新视角，去除干扰物以及极大地提高收敛速度，从而显著改进了现有技术，为 NeRF 在各种多样化的动态现实应用中开辟了新的研究方向。