Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation

2026-06-25

AI Summary

Millimeter-wave radar point clouds are commonly plagued by sparsity, high noise levels, and missing structural details, which severely limit downstream perception performance. To address these challenges, this work proposes a vision–radar fusion–based multimodal point cloud generation method. By leveraging image semantic segmentation as guidance, the approach introduces a depth–semantic alignment mechanism and an affinity-based fusion strategy to effectively align and integrate visual semantics with radar geometric information, enabling structure-constrained completion of sparse point clouds. The generated point clouds exhibit significantly enhanced density and structural completeness, leading to substantial improvements in accuracy and robustness for object detection and tracking, particularly in complex scenes.

Abstract

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.

Problem

• radar point cloud
• sparsity
• noise
• structural incompleteness
• 3D perception

Innovation

• Depth-Semantic Alignment
• Affinity-Guided Fusion
• Radar Point Cloud Generation
• Multimodal Fusion
• Sparse Completion