论文标题
学习对象级的点数增强器，用于半监督的3D对象检测
Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection
论文作者
论文摘要
半监督对象检测对于3D场景的理解很重要，因为在点云上获得大规模的3D边界框注释是时必的和劳动力密集的。现有的半监督方法通常采用教师认识的知识蒸馏以及增强策略来利用未标记的点云。但是，这些方法采用了全局增强，并具有场景级变换，因此对于实例级对象检测而言是最佳的。在这项工作中，我们提出了一个对象级点增强器（OPA），该点为半监督3D对象检测执行局部转换。这样，得出所得的增强器以强调对象实例而不是无关紧要的背景，从而使增强数据对对象探测器训练更有用。在扫描仪和Sun RGB-D数据集上进行的广泛实验表明，在各种实验环境下，提出的OPA对最新方法的表现良好。源代码将在https://github.com/nomiaro/opa上找到。
Semi-supervised object detection is important for 3D scene understanding because obtaining large-scale 3D bounding box annotations on point clouds is time-consuming and labor-intensive. Existing semi-supervised methods usually employ teacher-student knowledge distillation together with an augmentation strategy to leverage unlabeled point clouds. However, these methods adopt global augmentation with scene-level transformations and hence are sub-optimal for instance-level object detection. In this work, we propose an object-level point augmentor (OPA) that performs local transformations for semi-supervised 3D object detection. In this way, the resultant augmentor is derived to emphasize object instances rather than irrelevant backgrounds, making the augmented data more useful for object detector training. Extensive experiments on the ScanNet and SUN RGB-D datasets show that the proposed OPA performs favorably against the state-of-the-art methods under various experimental settings. The source code will be available at https://github.com/nomiaro/OPA.
