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.

翻译：我们从图像中找到高效且结构能见的 3D 场景代表。 Nerflets 是我们做出的关键贡献 -- -- 一组地方神经光亮场,它们合在一起代表一个场景。每个神经光亮场都保持自己的空间位置、方向和范围,在空间位置、方向和范围上,它们都有助于光学、密度和光度的重建。我们只能利用光度和推断光学图像监督,可以直接和共同优化一组神经光谱的参数,从而形成一个分解的场景代表,其中每个物体实例都由一组神经粒子代表。在与室内和室外环境进行实验时,我们发现该神经粒子:(1) 比传统的全球NERFs更适合和更高效地接近场景,(2) 允许从任意的视图中提取光谱和光度图像,(3) 能够让NERFs所罕见的任务(如3D光谱分割和交互式编辑) 。</s>