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.

翻译：三维实例分割对于需要全面理解三维场景的应用至关重要。本文提出一种同时学习系数与原型的创新方法。通过采用过完备采样策略，本方法生成一组过完备的实例预测结果，在推理阶段通过非极大值抑制（NMS）算法从中选择最优预测。所得原型具有可视化与可解释的特性。本方法在S3DIS-blocks数据集上展现出优越性能，在mRec与mPrec指标上持续超越现有方法。此外，其运行速度较当前最优方法提升32.9%。值得注意的是，本方法仅需总推理时间的0.8%，且推理时间方差较现有方法降低超过20倍。这些特性使得本方法非常适合需要快速推理与高可靠性的实际应用场景。