  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.
