Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy

TL;DR

This paper investigates how inaccuracies in RGB color information negatively affect the accuracy of point cloud semantic segmentation, emphasizing the importance of reassessing RGB's role in 3D scene understanding.

Contribution

It introduces a statistical method to quantify the impact of erroneous RGB colorization on segmentation performance, distinguishing between incorrect and similar color errors.

Findings

Both incorrect and similar color errors significantly degrade segmentation accuracy.

Similar color errors particularly impair geometric feature extraction.

RGB inaccuracies have a critical impact on segmentation performance.

Abstract

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.