# Fine-grained Metrics for Point Cloud Semantic Segmentation

Source: [Fine-grained Metrics for Point Cloud Semantic Segmentation](<https://www.sota2.com/research/paper/68fa74551e209131cea61296>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:07:41.757Z

Authors: Zhuheng Lu, Ting Wu, Yuewei Dai, Weiqing Li, Zhiyong Su

Published: 2024-07-31T02:25:30.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.21289>)

[PDF](<https://arxiv.org/pdf/2407.21289.pdf>)