用视觉语义指导雷达点云生成，提升稀疏点云质量与感知性能。
Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation
- 融合图像语义信息约束雷达点云结构，实现空间对齐。
- 通过稀疏补全策略增强点云密度，恢复缺失结构。
- 显著提升复杂环境下目标检测与跟踪的准确率和鲁棒性。
点云是三维空间信息的重要载体，其质量直接影响物体检测与跟踪等下游感知任务的表现。然而，毫米波雷达点云通常存在稀疏、噪声大、结构不完整等问题。为此，本文提出一种基于视觉-雷达融合的多模态点云生成方法。该方法利用图像语义信息对雷达点云施加结构约束并实现空间对齐，同时采用稀疏补全策略提升点云密度并恢复缺失结构。生成的点云在目标检测与跟踪任务中进行评估。实验结果表明，该方法有效提升了点云质量，增强了感知模型在复杂环境下的检测精度与鲁棒性，为多传感器点云生成与智能感知系统提供了实用解决方案。
原文摘要 · Abstract (English)
Point clouds are an important carrier of three-dimensional spatial information, and their quality directly affects the performance of downstream perception tasks such as object detection and tracking. However, millimeter-wave radar point clouds are typically sparse, noisy, and structurally incomplete. To address these limitations, this paper proposes a multimodal point cloud generation method based on vision-radar fusion. The proposed method leverages image semantic information to impose structural constraints and achieve spatial alignment for radar point clouds, while incorporating a sparse completion strategy to enhance point density and recover missing structures. The generated point clouds are further evaluated in object detection and tracking tasks. Experimental results demonstrate that the proposed method effectively improves point cloud quality and enhances the detection accuracy and robustness of perception models in complex environments, providing a practical solution for multisensor point cloud generation and intelligent perception systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。