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，模型与用户协作，共同描绘三维点云中的感兴趣对象。在迭代过程中，模型将每个数据点分配给某个对象（或背景），而用户则纠正分割结果中的错误并将其反馈给模型。当前最佳实践将此问题建模为二分类，并逐一分割对象。该模型期望用户提供正向点击来指示被错误分配到背景的区域，以及负向点击来指示被错误分配到对象的区域。逐一处理对象是低效的，因为它忽视了对象间的协同效应：从定义上看，对某个对象的正向点击也可作为邻近对象的负向点击。此外，相邻对象间的直接竞争能加速其共同边界的识别。我们提出AGILE3D——一种高效、基于注意力的模型，其具备以下特性：（1）支持多个三维对象的同步分割，（2）以更少的用户点击获得更精确的分割掩码，（3）提供更快的推理速度。核心思想是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间的显式交互。每次添加新的点击时，只需运行轻量级解码器即可生成更新的分割掩码。在四个不同的三维点云数据集实验中，AGILE3D达到了新的最优性能。此外，我们还通过真实用户研究验证了其在真实场景中的实用性。