  We address efficient and structure-aware 3D scene representation from images.
Nerflets are our key contribution -- a set of local neural radiance fields that
together represent a scene. Each nerflet maintains its own spatial position,
orientation, and extent, within which it contributes to panoptic, density, and
radiance reconstructions. By leveraging only photometric and inferred panoptic
image supervision, we can directly and jointly optimize the parameters of a set
of nerflets so as to form a decomposed representation of the scene, where each
object instance is represented by a group of nerflets. During experiments with
indoor and outdoor environments, we find that nerflets: (1) fit and approximate
the scene more efficiently than traditional global NeRFs, (2) allow the
extraction of panoptic and photometric renderings from arbitrary views, and (3)
enable tasks rare for NeRFs, such as 3D panoptic segmentation and interactive
editing.

Computer Vision and Pattern Recognition;Graphics