论文标题
弱监督的语义点云分段：标签少10倍
Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels
论文作者
论文摘要
点云分析最近受到了很多关注。细分是最重要的任务之一。现有方法的成功归因于深度网络设计和大量标记的培训数据，后者被认为始终可用。但是，在实践中，获得3D点云分割标签通常非常昂贵。在这项工作中，我们提出了一种弱监督的点云分段方法，该方法仅需要在训练阶段标记的一小部分点。通过学习梯度近似和对其他空间和色彩平滑度约束的开发，这是可能的。实验是在三个公共数据集上进行的，具有不同程度的监督。特别是，我们提出的方法可以产生靠近，有时甚至比其完全有监督的对应的结果，其标签少10美元。
Point cloud analysis has received much attention recently; and segmentation is one of the most important tasks. The success of existing approaches is attributed to deep network design and large amount of labelled training data, where the latter is assumed to be always available. However, obtaining 3d point cloud segmentation labels is often very costly in practice. In this work, we propose a weakly supervised point cloud segmentation approach which requires only a tiny fraction of points to be labelled in the training stage. This is made possible by learning gradient approximation and exploitation of additional spatial and color smoothness constraints. Experiments are done on three public datasets with different degrees of weak supervision. In particular, our proposed method can produce results that are close to and sometimes even better than its fully supervised counterpart with 10$\times$ fewer labels.
