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| Parameter | Value | |-----------|-------| | Scanner | Leica ScanStation P50 (0.5 mm accuracy, 360° FoV, 100 k points/s) | | Scan Resolution | 0.5 mm point spacing at 10 m distance | | Intensity Capture | Yes (laser return strength) | | Auxiliary | Optional RGB camera (for texture fusion) | | Positioning | GNSS‑RTK for global reference (≤ 2 cm) | | Scan Time | 4 min per full 360° sweep (≈ 12 M points) |

Table 1 summarises the state‑of‑the‑art and highlights the research gap addressed by this paper. lidar360 crack

The remainder of the paper is organised as follows: Section 2 reviews related work; Section 3 describes the LIDAR‑360‑Crack system; Section 4 details data acquisition and preprocessing; Section 5 explains the crack extraction methodology; Section 6 presents experiments and results; Section 7 discusses limitations and future directions; finally, Section 8 concludes the study. | Parameter | Value | |-----------|-------| | Scanner

If the cost of LiDAR360 is prohibitive, the geospatial community offers powerful open-source tools that are completely free and safe: Smith¹, B

A. Smith¹, B. Lee², C. Martínez³, D. Khan⁴

Early research focused on 2‑D image processing (Canny edge detection, morphological operations) [1]. More recent approaches employ convolutional neural networks (CNNs) such as CrackNet, DeepCrack, and FC‑Nets, achieving > 85 % F‑score on laboratory datasets [2‑4]. However, image‑only methods struggle with occlusions, uneven illumination, and lack depth information, which limits accurate width estimation.