Abstract
Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is less explored. In this work, we present 3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on point clouds. Following the idea of previous proposal-free instance segmentation approaches, our model learns a feature embedding and groups the obtained feature space into semantic instances. Current point-based methods scale linearly with the number of points by processing local sub-parts of a scene individually. However, to perform instance segmentation by clustering, globally consistent features are required. Therefore, we propose to combine local point geometry with global context information from an intermediate bird's-eye view representation.
Abstract (translated)
最近的深度学习模型通过直接在非结构化点云上操作，在三维场景分析任务上取得了令人印象深刻的结果。在对象分类和语义分割方面取得了很大的进展。然而，对实例分割的任务探索较少。在这项工作中，我们提出了3D-BEVIS，一个深度学习框架，用于点云上的3D语义实例分割。该模型借鉴了以往的无建议实例分割方法，学习了特征嵌入，并将获得的特征空间分组为语义实例。当前基于点的方法通过单独处理场景的局部子部分与点的数量成线性比例。但是，要通过集群进行实例分割，需要全局一致的特性。因此，我们建议将局部点几何与来自中间鸟瞰图表示的全局上下文信息相结合。
URL
https://arxiv.org/abs/1904.02199
