During interactive segmentation, a model and a user work together to delineate objects of interest in a 3D point cloud. In an iterative process, the model assigns each data point to an object (or the background), while the user corrects errors in the resulting segmentation and feeds them back into the model. The current best practice formulates the problem as binary classification and segments objects one at a time. The model expects the user to provide positive clicks to indicate regions wrongly assigned to the background and negative clicks on regions wrongly assigned to the object. Sequentially visiting objects is wasteful since it disregards synergies between objects: a positive click for a given object can, by definition, serve as a negative click for nearby objects. Moreover, a direct competition between adjacent objects can speed up the identification of their common boundary. We introduce AGILE3D, an efficient, attention-based model that (1) supports simultaneous segmentation of multiple 3D objects, (2) yields more accurate segmentation masks with fewer user clicks, and (3) offers faster inference. Our core idea is to encode user clicks as spatial-temporal queries and enable explicit interactions between click queries as well as between them and the 3D scene through a click attention module. Every time new clicks are added, we only need to run a lightweight decoder that produces updated segmentation masks. In experiments with four different 3D point cloud datasets, AGILE3D sets a new state-of-the-art. Moreover, we also verify its practicality in real-world setups with real user studies.

翻译：在交互式分割过程中，模型与用户协同工作，共同勾勒三维点云中的感兴趣目标。通过迭代流程，模型将每个数据点分配给某个目标（或背景），而用户则修正分割结果中的错误并将其反馈至模型。当前主流方法将问题建模为二分类任务，并逐一分割目标。该范式要求用户提供正向点击以标记被错误划为背景的区域，以及负向点击以标记被错误划为目标的区域。依次处理每个目标会忽略目标间的协同效应，造成效率浪费：从定义上看，针对某一目标的正向点击必然可作为邻近目标的负向点击。更重要的是，相邻目标间的直接竞争关系能加速其共同边界识别。我们提出AGILE3D——一种基于注意力机制的高效模型，其具备三大特性：(1)支持多个三维目标的同步分割；(2)用更少的用户点击获取更精确的分割掩膜；(3)实现更快的推理速度。核心创新在于将用户点击编码为时空查询，并通过点击注意力模块实现点击查询之间、以及点击查询与三维场景之间的显式交互。每次新增点击时，仅需运行轻量级解码器即可生成更新后的分割掩膜。在四个不同三维点云数据集上的实验表明，AGILE3D达到了新最优水平。此外，我们通过真实用户研究验证了其在现实场景中的实用性。