We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost.

提出了一种名为3DCFS的新型快速坚固的3D点云分割框架通过耦合特征选择，以联合执行语义和实例分割，并通过耦合特征选择模块CFSM自适应选择和融合两个任务中的互惠语义和实例特征来进一步提高实例分割任务的性能。

3DCFS: 基于耦合特征选择的快速、鲁棒联合三维语义实例分割