# Traffic Scene Semantic Segmentation Enhancement Based on Cylinder3D with Multi-Scale 3D Attention

## TL;DR

This paper improves 3D point cloud segmentation for traffic scenes using a new method that enhances accuracy and efficiency.

## Contribution

The paper introduces an improved point cloud segmentation method combining PointMamba and MS3DAM modules for better performance.

## Key findings

- The proposed method achieves a 64.98% mIoU on SemanticKITTI, a 2.81% improvement over Cylinder3D.
- The method maintains high efficiency due to the linear computational complexity of Mamba.
- The KAT module enhances the model's robustness and perceptual capacity for point cloud data.

## Abstract

With the rapid development of 3D sensor technology, point cloud semantic segmentation has found widespread applications in autonomous driving, remote sensing, mapping, and industrial manufacturing. However, outdoor traffic scenes present significant challenges: point clouds are inherently disordered, unevenly distributed, and unstructured. As a result, traditional point cloud semantic segmentation methods often suffer from low accuracy and unstable performance in complex tasks such as semantic segmentation and object detection. To address these limitations, this paper proposes an improved point cloud semantic segmentation method based on Cylinder3D. The proposed approach integrates the PointMamba and MS3DAM modules, which enhance the model’s ability to capture global features while preserving local details, thereby improving adaptability and recognition across multiple feature scales. Furthermore, leveraging the linear computational complexity of Mamba enables the method to maintain high efficiency when processing large-scale point cloud data. In addition, incorporating the KAT module into the encoder improves the model’s perceptual capacity and robustness in handling point clouds. Experimental results on the SemanticKITTI dataset demonstrate that the proposed method achieves a mean Intersection over Union (mIoU) of 64.98%, representing a 2.81% improvement over Cylinder3D, thereby confirming its superior segmentation accuracy compared with existing models.