张宁 Semantic Labeling of Indoor Environments from 3D RGB Maps
"链接：https://pan.baidu.com/s/1JtQfWlhynnXvh69VLmvAFg
提取码：7evy
基于三维RGB图的室内环境语义标注
Abstract— We present an approach to automatically assign semantic labels to rooms reconstructed from 3D RGB maps of apartments. Evidence for the room types is generated using state-of-the-art deep-learning techniques for scene classification and object detection based on automatically generated virtual RGB views, as well as from a geometric analysis of the map’s 3D structure. The evidence is merged in a conditional random field, using statistics mined from different datasets of indoor environments. We evaluate our approach qualitatively and quantitatively and compare it to related methods.
我们提出了一种自动为从公寓的3D RGB地图重建的房间分配语义标签的方法。 房间类型的证据是使用最先进的深度学习技术生成的，用于基于自动生成的虚拟RGB视图的场景分类和对象检测，以及地图3D结构的几何分析。 证据在条件随机字段中合并，使用从室内环境的不同数据集挖掘的统计数据。 我们定性和定量地评估我们的方法，并将其与相关方法进行比较。
泡泡一分钟：Semantic Labeling of Indoor Environments from 3D RGB Maps
原文：https://www.cnblogs.com/feifanrensheng/p/10606514.html