  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.
