Fine-grained Metrics for Point Cloud Semantic Segmentation

• URL: http://arxiv.org/abs/2407.21289v1
• Date: Wed, 31 Jul 2024 02:25:30 GMT
• Title: Fine-grained Metrics for Point Cloud Semantic Segmentation
• Authors: Zhuheng Lu, Ting Wu, Yuewei Dai, Weiqing Li, Zhiyong Su,
• Abstract summary: Two forms of imbalances are commonly observed in point cloud semantic segmentation datasets. The majority of categories and large objects are favored in the existing evaluation metrics. This paper suggests fine-grained mIoU and mAcc for a more thorough assessment of point cloud segmentation algorithms.
• Score: 6.713120348917712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Two forms of imbalances are commonly observed in point cloud semantic segmentation datasets: (1) category imbalances, where certain objects are more prevalent than others; and (2) size imbalances, where certain objects occupy more points than others. Because of this, the majority of categories and large objects are favored in the existing evaluation metrics. This paper suggests fine-grained mIoU and mAcc for a more thorough assessment of point cloud segmentation algorithms in order to address these issues. Richer statistical information is provided for models and datasets by these fine-grained metrics, which also lessen the bias of current semantic segmentation metrics towards large objects. The proposed metrics are used to train and assess various semantic segmentation algorithms on three distinct indoor and outdoor semantic segmentation datasets.

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