Joint prototype and coefficient prediction for 3D instance segmentation
<|reference_start|>Joint prototype and coefficient prediction for 3D instance segmentation: 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.<|reference_end|>
@article{royen2024joint,
    title={Joint prototype and coefficient prediction for 3D instance segmentation},
    author={Remco Royen, Leon Denis, Adrian Munteanu},
    journal={Electronics Letters 60.5 (2024): e13137},
    year={2024},
    doi={10.1049/ell2.13137},
    archivePrefix={arXiv},
    eprint={2407.06958},
    primaryClass={cs.CV}
}