Optimal Pre-processing of Laser Scanning Data for Indoor Scene Analysis and 3D Reconstruction of Building Models

Kim, Minju; Kim, Harim

doi:10.1007/s12205-023-2406-9

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…Despite the emergence of software tools capable of basic point cloud preprocessing [103,104] and partial unification of the most common procedures, specific and very application-dependent pre-processing activities exist [105]. Noise reduction, downsampling, filtering, transformation, registration or geo-registration, segmentation, classification, and alignment are the most commonly used methods [106][107][108][109]. These procedures can be combined with or become the primary operation of the point cloud management analyses for specific purposes.…”

Section: Introductionmentioning

confidence: 99%

“…These procedures can be combined with or become the primary operation of the point cloud management analyses for specific purposes. We can introduce geometric primitive calculation (curvature, surface normal vectors) as a pre-processing method for edge and boundary detection [110], topology building and improved segmentation application [106], statistical and geometry-based filtering [111], and semi and automatic features and objects extraction [110]. For forestry applications, ref.…”

Section: Introductionmentioning

confidence: 99%

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Mobile Laser Scanning Data Collected under a Forest Canopy with GNSS/INS-Positioned Systems: Possibilities of Processability Improvements

Čeňava,

Tuček,

Chudá

et al. 2024

Remote Sensing

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GNSS/INS-based positioning must be revised for forest mapping, especially inside the forest. This study deals with the issue of the processability of GNSS/INS-positioned MLS data collected in the forest environment. GNSS time-based point clustering processed the misaligned MLS point clouds collected from skid trails under a forest canopy. The points of a point cloud with two misaligned copies of the forest scene were manually clustered iteratively until two partial point clouds with the single forest scene were generated using a histogram of GNSS time. The histogram’s optimal bin width was the maximum bin width used to create the two correct point clouds. The influence of GNSS outage durations, signal strength statistics, and point cloud parameters on the optimal bin width were then analyzed using correlation and regression analyses. The results showed no significant influence of GNSS outage duration or GNSS signal strength from the time range of scanning the two copies of the forest scene on the optimal width. The optimal bin width was strongly related to the point distribution in time, especially by the duration of the scanned plot’s occlusion from reviewing when the maximum occlusion period influenced the optimal bin width the most (R2 = 0.913). Thus, occlusion of the sub-plot scanning of tree trunks and the terrain outside it improved the processability of the MLS data. Therefore, higher stem density of a forest stand is an advantage in mapping as it increases the duration of the occlusions for a point cloud after it is spatially tiled.

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