论文标题
sceneencoder：带有可学习场景描述符的点云的场景感知语义分割
SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor
论文作者
论文摘要
除本地功能外，全球信息在语义细分中起着至关重要的作用，而最近的作品通常无法明确提取有意义的全球信息并充分利用它。在本文中，我们提出了一个场景编码模块，以施加场景感知的指导，以增强全球信息的效果。该模块预测了一个场景描述符，该场景描述器学会表示场景中存在的对象类别，并通过过滤不属于此场景的类别直接指导点级的语义分割。此外，为了减轻局部区域的分割噪声，我们设计了一个区域相似性损失，以相同的标签传播到自己的相邻点的区分特征，从而增强了点智能特征的区别能力。我们将方法集成到几个流行的网络中，并在基准数据集扫描仪和Shapenet上进行广泛的实验。结果表明，我们的方法大大提高了基准的性能并实现最先进的性能。
Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it. In this paper, we propose a SceneEncoder module to impose a scene-aware guidance to enhance the effect of global information. The module predicts a scene descriptor, which learns to represent the categories of objects existing in the scene and directly guides the point-level semantic segmentation through filtering out categories not belonging to this scene. Additionally, to alleviate segmentation noise in local region, we design a region similarity loss to propagate distinguishing features to their own neighboring points with the same label, leading to the enhancement of the distinguishing ability of point-wise features. We integrate our methods into several prevailing networks and conduct extensive experiments on benchmark datasets ScanNet and ShapeNet. Results show that our methods greatly improve the performance of baselines and achieve state-of-the-art performance.
