研究点云颜色错误如何影响语义分割精度，发现相似色误判更影响几何特征提取。
Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy
- 区分颜色错误与相似色错误，统计分析其对分割的影响。
- 颜色错误使分割准确率下降，相似色问题尤其损害几何特征识别。
- 提醒未来算法需重新评估RGB信息的可靠性，适合3D视觉研究者阅读。
点云语义分割是将每个点分类到预定义类别中的关键任务，对三维场景理解至关重要。尽管基于图像的分割方法因成熟而广泛应用，但仅依赖RGB信息的方法常受颜色不准确影响，导致性能下降。近期研究引入强度和几何信息等额外特征，但当颜色化出现误差时，RGB通道仍会显著降低分割准确率。然而，此前研究未系统量化颜色错误的影响。本文提出一种新的统计方法，评估不准确的RGB信息对基于图像的点云分割的影响。我们将RGB错误分为两类：错误颜色信息和相似颜色信息。结果表明，两类错误均显著降低分割准确率，其中相似颜色错误尤其影响几何特征的提取。研究强调必须重新审视RGB信息在点云分割中的作用，并为未来算法设计提供重要启示。
原文摘要 · Abstract (English)
Point cloud semantic segmentation, the process of classifying each point into predefined categories, is essential for 3D scene understanding. While image-based segmentation is widely adopted due to its maturity, methods relying solely on RGB information often suffer from degraded performance due to color inaccuracies. Recent advancements have incorporated additional features such as intensity and geometric information, yet RGB channels continue to negatively impact segmentation accuracy when errors in colorization occur. Despite this, previous studies have not rigorously quantified the effects of erroneous colorization on segmentation performance. In this paper, we propose a novel statistical approach to evaluate the impact of inaccurate RGB information on image-based point cloud segmentation. We categorize RGB inaccuracies into two types: incorrect color information and similar color information. Our results demonstrate that both types of color inaccuracies significantly degrade segmentation accuracy, with similar color errors particularly affecting the extraction of geometric features. These findings highlight the critical need to reassess the role of RGB information in point cloud segmentation and its implications for future algorithm design.
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