Enhancing the Utilization of Color Information in Point Cloud Semantic Segmentation

Point cloud semantic segmentation is crucial in various applications such as autonomous driving, robotics, and virtual reality, aiming to assign labels to each point in a cloud to reflect spatial relationships and boundaries. While previous methods primarily focus on geometric features, they often overlook the auxiliary role of color information, especially in scenes where geometric structures are less distinct. In this paper, we propose the Color Point Cloud Enhancement (CPCE) method to effectively leverage color information for improved 3D scene understanding. CPCE introduces a color information enhancement module with multi-scale consistency, enriching point features throughout the encoder stages. Additionally, we develop a novel contrastive learning module that uses relative color coordinates for point cloud serialization, allowing for the capture of positive and negative samples from distant points with similar color textures. Furthermore, we design a contrastive learning module tailored for scenes with weak geometric structures, enhancing feature representation through color-augmented contrast. Our method achieved a 78.1% mIoU on the ScanNet dataset, outperforming existing models trained on a single dataset. These results highlight the effectiveness of CPCE in scenarios where traditional methods struggle, particularly in enhancing segmentation accuracy by utilizing color as a critical feature.