This paper presents a novel framework for compactly representing a 3D indoor scene using a set of polycuboids through a deep learning-based fitting method. Indoor scenes mainly consist of man-made objects, such as furniture, which often exhibit rectilinear geometry. This property allows indoor scenes to be represented using combinations of polycuboids, providing a compact representation that benefits downstream applications like furniture rearrangement. Our framework takes a noisy point cloud as input and first detects six types of cuboid faces using a transformer network. Then, a graph neural network is used to validate the spatial relationships of the detected faces to form potential polycuboids. Finally, each polycuboid instance is reconstructed by forming a set of boxes based on the aggregated face labels. To train our networks, we introduce a synthetic dataset encompassing a diverse range of cuboid and polycuboid shapes that reflect the characteristics of indoor scenes. Our framework generalizes well to real-world indoor scene datasets, including Replica, ScanNet, and scenes captured with an iPhone. The versatility of our method is demonstrated through practical applications, such as virtual room tours and scene editing.

翻译：本文提出了一种新颖的框架，通过基于深度学习的拟合方法，利用多立方体集合紧凑地表示三维室内场景。室内场景主要由人造物体（如家具）构成，这些物体通常呈现直线几何特征。该特性使得室内场景能够通过多立方体的组合进行表示，从而为家具重排等下游应用提供紧凑的表示形式。我们的框架以含噪声的点云作为输入，首先使用Transformer网络检测六种类型的立方体表面。随后，采用图神经网络验证检测表面的空间关系以构建潜在的多立方体。最后，通过基于聚合表面标签生成立方体集合来重建每个多立方体实例。为训练网络，我们引入了涵盖多种立方体与多立方体形状的合成数据集，该数据集充分体现了室内场景的特征。我们的框架在真实室内场景数据集（包括Replica、ScanNet及iPhone采集的场景）上展现出良好的泛化能力。通过虚拟房间漫游和场景编辑等实际应用，验证了本方法的通用性。