We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive
deep learning framework for 3D object instance segmentation on point clouds.
SGPN uses a single network to predict point grouping proposals and a
corresponding semantic class for each proposal, from which we can directly
extract instance segmentation results. Important to the effectiveness of SGPN
is its novel representation of 3D instance segmentation results in the form of
a similarity matrix that indicates the similarity between each pair of points
in embedded feature space, thus producing an accurate grouping proposal for
each point. To the best of our knowledge, SGPN is the first framework to learn
3D instance-aware semantic segmentation on point clouds. Experimental results
on various 3D scenes show the effectiveness of our method on 3D instance
segmentation, and we also evaluate the capability of SGPN to improve 3D object
detection and semantic segmentation results. We also demonstrate its
flexibility by seamlessly incorporating 2D CNN features into the framework to
boost performance.

本研究提出了一种基于深度学习的简单易用的三维物体实例分割框架，称为相似性分组提案网络（SGPN），该框架使用单个网络预测点分组提案和相应的语义类别，并可直接提取实例分割结果，实验表明，该框架在点云数据上的三维实例分割任务上具有很高的准确性，而且可提高物体检测和语义分割结果的效果。