Joint prototype and coefficient prediction for 3D instance segmentation

• URL: http://arxiv.org/abs/2407.06958v1
• Date: Tue, 9 Jul 2024 15:36:13 GMT
• Title: Joint prototype and coefficient prediction for 3D instance segmentation
• Authors: Remco Royen, Leon Denis, Adrian Munteanu,
• Abstract summary: 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. Our method demonstrates superior performance on S3DIS-blocks, consistently outperforming existing methods in mRec and mPrec. 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.
• Score: 6.632158868486343
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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