房间包络：用于从图像进行室内布局重建的合成数据集
Room Envelopes: A Synthetic Dataset for Indoor Layout Reconstruction from Images
AI总结：
本文提出Room Envelopes合成数据集，通过提供可见表面与结构布局点图，监督单目前馈几何估计器同时预测两者，从而提升室内场景范围、物体形状和位置的重建理解。
摘要：
现代场景重建方法能够准确恢复在一幅或多幅图像中可见的3D表面。然而，这会导致重建不完整，缺失所有被遮挡的表面。尽管在使用生成模型根据部分观测重建完整物体方面已取得很大进展，但场景的结构性元素，如墙壁、地板和天花板，受到的关注较少。我们认为这些场景元素应该相对容易预测，因为它们通常是平面的、重复的且简单的，因此可能适合采用成本较低的方法。在这项工作中，我们提出了一个合成数据集——Room Envelopes——通过为每张图像提供一组RGB图像和两个关联的点图来促进该任务的进展：一个捕获可见表面，另一个捕获移除设备和固定装置后的第一个表面，即结构布局。正如我们所展示的，这能够为前馈单目几何估计器提供直接监督，使其同时预测第一个可见表面和第一个布局表面。这赋予了对场景范围以及其物体的形状和位置的理解。
英文摘要：
Modern scene reconstruction methods are able to accurately recover 3D surfaces that are visible in one or more images. However, this leads to incomplete reconstructions, missing all occluded surfaces. While much progress has been made on reconstructing entire objects given partial observations using generative models, the structural elements of a scene, like the walls, floors and ceilings, have received less attention. We argue that these scene elements should be relatively easy to predict, since they are typically planar, repetitive and simple, and so less costly approaches may be suitable. In this work, we present a synthetic dataset -- Room Envelopes -- that facilitates progress on this task by providing a set of RGB images and two associated pointmaps for each image: one capturing the visible surface and one capturing the first surface once fittings and fixtures are removed, that is, the structural layout. As we show, this enables direct supervision for feed-forward monocular geometry estimators that predict both the first visible surface and the first layout surface. This confers an understanding of the scene's extent, as well as the shape and location of its objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
