用扩散模型从稀疏点云恢复深度图，实现零样本砖级缺陷检测。
InfraDiffusion: zero-shot depth map restoration with diffusion models and prompted segmentation from sparse infrastructure point clouds
- 通过虚拟相机投影点云生成深度图，再用DDNM修复
- 零样本下提升深度图清晰度与几何一致性，砖级分割准确率显著提高
- 适合低光照环境下桥梁隧道的自动化巡检，无需额外训练
点云广泛用于基础设施监测，提供几何信息，但其通常稀疏、无结构且噪声多，难以支持细粒度分割。现有方法多依赖高分辨率图像进行砖级缺陷（如剥落、灰浆损失）识别，但在低光环境（如砖石隧道）中难以获取。本文提出InfraDiffusion，一种零样本框架：将砖石点云通过虚拟相机投影为深度图，并利用去噪扩散空域模型（DDNM）进行恢复。无需任务特定训练，该方法显著提升了深度图的视觉清晰度与几何一致性。在桥隧点云数据集上的实验表明，结合通用分割模型SAM后，砖级分割性能大幅提升，展现出对砖石资产自动化检测的巨大潜力。代码与数据已开源。
原文摘要 · Abstract (English)
Point clouds are widely used for infrastructure monitoring by providing geometric information, where segmentation is required for downstream tasks such as defect detection. Existing research has automated semantic segmentation of structural components, while brick-level segmentation (identifying defects such as spalling and mortar loss) has been primarily conducted from RGB images. However, acquiring high-resolution images is impractical in low-light environments like masonry tunnels. Point clouds, though robust to dim lighting, are typically unstructured, sparse, and noisy, limiting fine-grained segmentation. We present InfraDiffusion, a zero-shot framework that projects masonry point clouds into depth maps using virtual cameras and restores them by adapting the Denoising Diffusion Null-space Model (DDNM). Without task-specific training, InfraDiffusion enhances visual clarity and geometric consistency of depth maps. Experiments on masonry bridge and tunnel point cloud datasets show significant improvements in brick-level segmentation using the Segment Anything Model (SAM), underscoring its potential for automated inspection of masonry assets. Our code and data is available at https://github.com/Jingyixiong/InfraDiffusion-official-implement.
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