We describe a method to parse a complex, cluttered indoor scene into primitives which offer a parsimonious abstraction of scene structure. Our primitives are simple convexes. Our method uses a learned regression procedure to parse a scene into a fixed number of convexes from RGBD input, and can optionally accept segmentations to improve the decomposition. The result is then polished with a descent method which adjusts the convexes to produce a very good fit, and greedily removes superfluous primitives. Because the entire scene is parsed, we can evaluate using traditional depth, normal, and segmentation error metrics. Our evaluation procedure demonstrates that the error from our primitive representation is comparable to that of predicting depth from a single image.

翻译：我们描述了一种方法，将复杂、杂乱的室内场景解析为能够简洁抽象场景结构的基元。我们的基元是简单的凸体。该方法通过学习的回归过程，从RGBD输入中将场景解析为固定数量的凸体，并可选择接受分割掩码以改进分解结果。随后通过下降法对结果进行优化，调整凸体使其达到高度拟合，并贪婪地去除多余的基元。由于解析了完整场景，我们可以使用传统的深度、法线和分割误差度量进行评估。评估过程表明，我们的基元表示的误差与从单张图像预测深度的误差相当。