  While Neural Radiance Fields (NeRFs) had achieved unprecedented novel view
synthesis results, they have been struggling in dealing with large-scale
cluttered scenes with sparse input views and highly view-dependent appearances.
Specifically, existing NeRF-based models tend to produce blurry rendering with
the volumetric reconstruction often inaccurate, where a lot of reconstruction
errors are observed in the form of foggy "floaters" hovering within the entire
volume of an opaque 3D scene. Such inaccuracies impede NeRF's potential for
accurate 3D NeRF registration, object detection, segmentation, etc., which
possibly accounts for only limited significant research effort so far to
directly address these important 3D fundamental computer vision problems to
date. This paper analyzes the NeRF's struggles in such settings and proposes
Clean-NeRF for accurate 3D reconstruction and novel view rendering in complex
scenes. Our key insights consist of enforcing effective appearance and geometry
constraints, which are absent in the conventional NeRF reconstruction, by 1)
automatically detecting and modeling view-dependent appearances in the training
views to prevent them from interfering with density estimation, which is
complete with 2) a geometric correction procedure performed on each traced ray
during inference. Clean-NeRF can be implemented as a plug-in that can
immediately benefit existing NeRF-based methods without additional input. Codes
will be released.
