论文标题
快速3D室内场景合成，具有离散和精确的布局模式提取
Fast 3D Indoor Scene Synthesis with Discrete and Exact Layout Pattern Extraction
论文作者
论文摘要
我们为室内场景综合提供了一个快速的框架，给定一个房间的几何形状和带有博学的先验的对象列表。与现有的数据驱动的解决方案不同，该解决方案通常通过共发生分析和统计模型拟合提取先验，我们的方法通过测试通过测试完全空间随机性（CSR）来测量空间关系的优势，并根据样品提取复杂的先验，并具有准确表示离散布局的能力。借助提取的先验，我们的方法通过将输入对象划分为不相交组，从而实现加速度和合理性，然后基于Hausdorff Metric进行布局优化。广泛的实验表明，我们的框架能够测量对象之间更合理的关系，并同时在几秒钟内产生各种布置。
We present a fast framework for indoor scene synthesis, given a room geometry and a list of objects with learnt priors. Unlike existing data-driven solutions, which often extract priors by co-occurrence analysis and statistical model fitting, our method measures the strengths of spatial relations by tests for complete spatial randomness (CSR), and extracts complex priors based on samples with the ability to accurately represent discrete layout patterns. With the extracted priors, our method achieves both acceleration and plausibility by partitioning input objects into disjoint groups, followed by layout optimization based on the Hausdorff metric. Extensive experiments show that our framework is capable of measuring more reasonable relations among objects and simultaneously generating varied arrangements in seconds.
