论文标题
3D中的室内场景识别
Indoor Scene Recognition in 3D
论文作者
论文摘要
认识到一个类型的环境，一个人的位置是一项重要的感知任务。例如，对于在室内操作的机器人，知道它是在厨房，走廊还是卧室里。现有方法试图根据2D图像或2.5D范围图像对场景进行分类。在这里，我们研究了3D点云（或体素）数据的场景识别，并表明它的表现大大优于基于2D鸟类视图的方法。此外，我们主张多任务学习是改善场景识别的一种方式，基于场景类型与场景中的对象高度相关，因此将其语义分割成不同的对象类。在一系列消融研究中，我们表明，成功的场景识别不仅是对某些场景类型（例如浴缸）所独有的对象的识别，还取决于几种不同的提示，包括粗3D几何，颜色和对象类别的（隐式）分布。此外，我们证明了令人惊讶的稀疏3D数据足以以良好的精度对室内场景进行分类。
Recognising in what type of environment one is located is an important perception task. For instance, for a robot operating in indoors it is helpful to be aware whether it is in a kitchen, a hallway or a bedroom. Existing approaches attempt to classify the scene based on 2D images or 2.5D range images. Here, we study scene recognition from 3D point cloud (or voxel) data, and show that it greatly outperforms methods based on 2D birds-eye views. Moreover, we advocate multi-task learning as a way of improving scene recognition, building on the fact that the scene type is highly correlated with the objects in the scene, and therefore with its semantic segmentation into different object classes. In a series of ablation studies, we show that successful scene recognition is not just the recognition of individual objects unique to some scene type (such as a bathtub), but depends on several different cues, including coarse 3D geometry, colour, and the (implicit) distribution of object categories. Moreover, we demonstrate that surprisingly sparse 3D data is sufficient to classify indoor scenes with good accuracy.
