Zero-shot depth map restoration from sparse infrastructure point clouds using diffusion models and prompted segmentation

Point clouds are essential for infrastructure monitoring, but are often sparse and noisy, limiting fine-grained segmentation required for downstream tasks such as defect detection. Existing studies focus on semantic segmentation of large components or brick-level segmentation from RGB images, which are impractical in low-light environments such as masonry tunnels. This paper presents InfraDiffusion, a zero-shot framework that projects masonry point clouds into depth maps and restores them using an adapted Denoising Diffusion Null-space Model (DDNM). Without task-specific training, InfraDiffusion enhances the visual quality of depth maps, enabling downstream analysis on sparse data. Experiments on masonry bridge and tunnel datasets demonstrate significant improvements in brick-level segmentation: the mean Intersection over Union (mIoU) using the Segment Anything Model (SAM) increased from below 0.1 to over 0.7 for bridges and from below 0.4 to above 0.7 for tunnels. These results highlight InfraDiffusion’s potential for automated inspection of masonry assets under challenging sensing conditions.