We propose an approach for quickly building a visual representation of a full indoor building. Our goal is to enable intelligent systems which frequently and regularly monitor buildings to assist personnel operating remotely, a need of special importance in these days. Prior work in neural scene representations for view synthesis focuses on single objects and small scenes and does not scale to full buildings in short timeframes. We propose introducing the floorplan and learning a neural floorplan radiance field, mapping floorplan 3D points and view directions to emitted radiance, and rendering via a sinusoidal multi-layer perceptron (MLP) neural renderer. To incorporate local priors and further accelerate the overall learning, we use a hypernetwork which maps a floorplan surface normal to the parameters of the neural renderer, thus defining the scene by a space of local neural rendering functions across the building. This allows shared knowledge, reasoned in function space, of performing the neural rendering from various vantage points in the scene based on similar building structure represented in the floorplan surface normal, and facilitates meta-knowledge pre-training across multiple buildings. The meta-knowledge is used to initialize the parameters of the hypernetwork at test time for the target building. Our approach performs significantly accelerated learning of neural floorplan radiance fields in around 15 min for full buildings on a single commodity GPU, and renders in real-time at 64 Hz, allowing for immersive visual experiences.

我们提出了一种快速构建完整的室内建筑物的视觉表示的方法。我们的目标是使智能系统能够定期监控建筑物，以协助远程操作人员，这在当今尤其重要。用于视图合成的神经场景表示的先前工作集中在单个对象和小场景上，并且在短时间内无法缩放到整个建筑物。我们建议介绍平面图并学习神经平面图辐射场，将平面图3D点和视图方向映射到发射的辐射，并通过正弦多层感知器（MLP）神经渲染器进行渲染。为了结合本地先验知识并进一步加快整体学习速度，我们使用了一个超网络，该网络映射了一个与神经渲染器的参数垂直的平面图表面，从而通过整个建筑物中局部神经渲染功能的空间来定义场景。这允许在功能空间中推理的共享知识，这些知识基于平面图表面法线中表示的相似建筑结构从场景中的各个有利位置执行神经渲染，并有助于进行元知识在多座建筑物中进行预训练。元知识用于在测试时针对目标建筑物初始化超网络的参数。我们的方法在单个商品GPU上的完整建筑物中，在大约15分钟内显着加速了神经平面规划辐射场的学习，并以64 Hz实时渲染，从而提供了身临其境的视觉体验。