  3D instance segmentation is crucial for applications demanding comprehensive
3D scene understanding. In this paper, we introduce a novel method that
simultaneously learns coefficients and prototypes. Employing an overcomplete
sampling strategy, our method produces an overcomplete set of instance
predictions, from which the optimal ones are selected through a Non-Maximum
Suppression (NMS) algorithm during inference. The obtained prototypes are
visualizable and interpretable. Our method demonstrates superior performance on
S3DIS-blocks, consistently outperforming existing methods in mRec and mPrec.
Moreover, it operates 32.9% faster than the state-of-the-art. Notably, with
only 0.8% of the total inference time, our method exhibits an over 20-fold
reduction in the variance of inference time compared to existing methods. These
attributes render our method well-suited for practical applications requiring
both rapid inference and high reliability.
