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.

描述了一种将复杂、混乱的室内场景解析为简化的基本结构的方法。这种方法使用学习回归过程将场景从 RGBD 输入解析为固定数量的凸多边形，并可以选择性地接受分割以改善分解。然后使用下降方法调整凸多边形以获得很好的拟合度，并贪婪地去除冗余的基本结构。因为对整个场景进行了解析，所以可以使用传统的深度、法线和分割误差指标进行评估。评估过程表明，我们的基本结构表示的误差与从单个图像预测深度的误差相当。