论文标题
半监督3D形状分割，具有多级一致性和零件替代
Semi-supervised 3D shape segmentation with multilevel consistency and part substitution
论文作者
论文摘要
缺乏细粒度的3D形状分割数据是开发基于学习的3D分割技术的主要障碍。我们提出了一种有效的半监督方法，用于从几个标记的3D形状和大量未标记的3D数据中学习3D分割。对于未标记的数据，我们提出了一种新颖的多级一致性损失，以在多个级别的3D形状的扰动副本之间实施网络预测的一致性：点级，零件级和层次结构级别。对于标记的数据，我们开发了一种简单而有效的零件替代方案，以增强具有更大结构变化的标记的3D形状以增强训练。我们的方法已在partnet和shapenetpart上的3D对象语义分割的任务中得到了广泛的验证，以及扫描仪上的室内场景语义分割。它表现出与现有的半监督和无监督的预训练3D方法相比的表现。我们的代码和训练有素的模型可在https://github.com/isunchy/semi_supervise_3d_segmentation上公开获得。
The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data. For the unlabeled data, we present a novel multilevel consistency loss to enforce consistency of network predictions between perturbed copies of a 3D shape at multiple levels: point-level, part-level, and hierarchical level. For the labeled data, we develop a simple yet effective part substitution scheme to augment the labeled 3D shapes with more structural variations to enhance training. Our method has been extensively validated on the task of 3D object semantic segmentation on PartNet and ShapeNetPart, and indoor scene semantic segmentation on ScanNet. It exhibits superior performance to existing semi-supervised and unsupervised pre-training 3D approaches. Our code and trained models are publicly available at https://github.com/isunchy/semi_supervised_3d_segmentation.
