Reconstructing outdoor 3D scenes from temporal observations is a challenge
that recent work on neural fields has offered a new avenue for. However,
existing methods that recover scene properties, such as geometry, appearance,
or radiance, solely from RGB captures often fail when handling poorly-lit or
texture-deficient regions. Similarly, recovering scenes with scanning LiDAR
sensors is also difficult due to their low angular sampling rate which makes
recovering expansive real-world scenes difficult. Tackling these gaps, we
introduce Gated Fields - a neural scene reconstruction method that utilizes
active gated video sequences. To this end, we propose a neural rendering
approach that seamlessly incorporates time-gated capture and illumination. Our
method exploits the intrinsic depth cues in the gated videos, achieving precise
and dense geometry reconstruction irrespective of ambient illumination
conditions. We validate the method across day and night scenarios and find that
Gated Fields compares favorably to RGB and LiDAR reconstruction methods. Our
code and datasets are available at this https URL

通过利用时间观察重建室外三维场景是一个具有挑战性的问题，本文介绍了一种利用神经场的新方法 - Gated Fields，它结合了主动门控视频序列和光照，在各种环境光照条件下实现了精确且密集的几何重建。