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.

翻译：神经辐射场（NeRFs）在多视角静态场景图像合成逼真视图方面取得了显著成功，但在动态的真实世界环境中，面对移动物体、阴影和光照变化等干扰因素时仍面临挑战。现有方法能够处理受控环境和低遮挡率场景，但在渲染质量方面存在不足，尤其在高遮挡情况下表现不佳。本文提出NeRF On-the-go，这是一种简单而有效的方法，能够仅从随意采集的图像序列中，在复杂野外场景中实现鲁棒的新视图合成。通过深入探究不确定性，我们的方法不仅能有效消除干扰物（即使其在采集数据中占主导地位），还能实现显著更快的收敛速度。通过对多种场景的综合实验，本方法相较于现有最先进技术展现出显著提升。这一进展为NeRF在多样化动态现实世界应用开辟了新途径。