This paper tackles a 2D architecture vectorization problem, whose task is to infer an outdoor building architecture as a 2D planar graph from a single RGB image. We provide a new benchmark with ground-truth annotations for 2,001 complex buildings across the cities of Atlanta, Paris, and Las Vegas. We also propose a novel algorithm utilizing 1) convolutional neural networks (CNNs) that detects geometric primitives and classifies their relationships and 2) an integer programming (IP) that assembles the information into a 2D planar graph. While being a trivial task for human vision, the inference of a graph structure with an arbitrary topology is still an open problem for computer vision. Qualitative and quantitative evaluations demonstrate that our algorithm makes significant improvements over the current state-of-the-art, towards an intelligent system at the level of human perception. We will share code and data to promote further research.
翻译:本文解决了2D建筑矢量化问题, 任务是从一个 RGB 图像中将户外建筑结构作为 2D 平面图来推断出来。 我们为亚特兰大、巴黎和拉斯维加斯各城市的2 001座复杂建筑提供了新的地貌说明基准。 我们还提出一个新的算法, 利用1) 进化神经网络来检测几何原始并区分其关系; 2) 将信息汇集到2D 平面图中的整数程序(IP) 。 虽然这是一个微不足道的任务, 带有任意地形图的图形结构的推论仍然是计算机视觉的开放问题。 定性和定量评估表明, 我们的算法大大改进了当前的最新技术, 转向人类认知层面的智能系统。 我们将共享代码和数据, 以促进进一步的研究 。