论文标题
联合实例和语义分割的双向关注点云
Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds
论文作者
论文摘要
点云中的实例分割是理解3D场景的最细粒度的方法之一。由于其与语义细分的密切关系，许多作品同时处理了这两个任务，并利用了多任务学习的好处。但是，其中大多数仅考虑了简单的策略，例如元素特征融合，这可能不会导致相互促进。在这项工作中，我们在3D点云知觉的主链神经网络上构建了一个双向注意模块，该模块使用从一个任务的功能中测量的相似性矩阵来帮助汇总其他任务的非本地信息，从而避免潜在的功能排除和任务冲突。从对S3DIS数据集和Partnet数据集的全面实验和消融研究，我们的方法的优越性得到了验证。此外，还分析了双向注意模块如何帮助联合实例和语义分割的机制。
Instance segmentation in point clouds is one of the most fine-grained ways to understand the 3D scene. Due to its close relationship to semantic segmentation, many works approach these two tasks simultaneously and leverage the benefits of multi-task learning. However, most of them only considered simple strategies such as element-wise feature fusion, which may not lead to mutual promotion. In this work, we build a Bi-Directional Attention module on backbone neural networks for 3D point cloud perception, which uses similarity matrix measured from features for one task to help aggregate non-local information for the other task, avoiding the potential feature exclusion and task conflict. From comprehensive experiments and ablation studies on the S3DIS dataset and the PartNet dataset, the superiority of our method is verified. Moreover, the mechanism of how bi-directional attention module helps joint instance and semantic segmentation is also analyzed.
