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：我们提出一种方法，可将复杂杂乱的室内场景解析为能够提供场景结构简约抽象的基本体素。这些基本体素采用简单凸体形式。该方法通过学习的回归流程，将RGBD输入解析为固定数量的凸体，并可选择接受分割结果以改进分解效果。随后使用下降法对结果进行优化调整，使凸体达到最佳拟合状态，并贪婪地移除多余的基本体素。由于实现了全场景解析，我们能够采用传统的深度误差、法向误差和分割误差指标进行评估。实验评估表明，我们的基本体素表示产生的误差与单图像深度估计的误差水平相当。