论文标题
与单点监督的3D实例分割的多个实例图的协作传播
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision
论文作者
论文摘要
由于其广泛的应用，尤其是在现场理解领域，因此在3D点云上进行的实例细分一直在吸引越来越多的关注。但是，大多数现有的方法都在完全注释的数据上运行，同时手动准备点级的地面真相标签非常繁琐且劳动力密集。为了解决这个问题，我们提出了一种新颖的弱监督方法RWSEG，该方法仅需要用一个点标记一个对象。使用这些稀疏的弱标签，我们引入了一个带有两个分支的统一框架，分别使用自我注意力和跨刻画随机行走方法将语义和实例信息分别传播到未知区域。具体来说，我们提出了一种跨读竞争的随机步行（CRW）算法，该算法鼓励不同实例图之间的竞争以解决紧密放置对象中的歧义，从而提高了实例分配的准确性。 RWSEG生成高质量的实例级伪标签。 Scannet-V2和S3DIS数据集的实验结果表明，我们的方法通过完全监督的方法实现了可比性的性能，并且通过一个实质性的边距胜过以前的弱监督方法。
Instance segmentation on 3D point clouds has been attracting increasing attention due to its wide applications, especially in scene understanding areas. However, most existing methods operate on fully annotated data while manually preparing ground-truth labels at point-level is very cumbersome and labor-intensive. To address this issue, we propose a novel weakly supervised method RWSeg that only requires labeling one object with one point. With these sparse weak labels, we introduce a unified framework with two branches to propagate semantic and instance information respectively to unknown regions using self-attention and a cross-graph random walk method. Specifically, we propose a Cross-graph Competing Random Walks (CRW) algorithm that encourages competition among different instance graphs to resolve ambiguities in closely placed objects, improving instance assignment accuracy. RWSeg generates high-quality instance-level pseudo labels. Experimental results on ScanNet-v2 and S3DIS datasets show that our approach achieves comparable performance with fully-supervised methods and outperforms previous weakly-supervised methods by a substantial margin.
