Iskanje po katalogu digitalne knjižnice

Opis: Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to realworld applications where explainability and robustness are paramount.
Ključne besede: explainable AI, point cloud data, counterfactual reasoning, LiDAR, 3D point cloud data, interpretability, human-centered AI, large language models, K-nearest neighbors
Objavljeno v DKUM: 19.05.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (7,24 MB)

Opis: For successful dosing of plant protection products, the characteristics of the vine canopies should be known, based on which the spray amount should be dosed. In the field experiment, we compared two optical experimental methods, terrestrial lidar and aerial photogrammetry, with manual defoliation of some selected vines. Like those of other authors, our results show that both terrestrial lidar and aerial photogrammetry were able to represent the canopy well with correlation coefficients around 0.9 between the measured variables and the number of leaves. We found that in the case of aerial photogrammetry, significantly more points were found in the point cloud, but this depended on the choice of the ground sampling distance. Our results show that in the case of aerial UAS photogrammetry, subdividing the vine canopy segments to 5 × 5 cm gives the best representation of the volume of vine canopies.
Ključne besede: precision agriculture, remote sensing, 3D point clouds, vineyard, canopy reconstruction, terrestrial lidar, aerial photogrammetry, manual defoliation
Objavljeno v DKUM: 15.07.2024; Ogledov: 123; Prenosov: 11
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Ključne besede: deep learning, fully convolutional neural network, LiDAR data, digital elevation model, solar energy, solar potential
Objavljeno v DKUM: 26.01.2024; Ogledov: 244; Prenosov: 114
Celotno besedilo (6,42 MB)
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Opis: Automatic building reconstruction from laser-scanned data remains a challenging research topic due to buildings’ roof complexity and sparse data. A novel automatic building reconstruction methodology, based on half-spaces and a height jump analysis, is presented in this paper. The proposed methodology is performed in three stages. During the preprocessing stage, the classified input point cloud is clustered by position to obtain building point sets, which are then evaluated to obtain half-spaces and detect height jumps. Half-spaces represent the fundamental shape for generating building models, and their definition is obtained from the corresponding segment of points that describe an individual planar surface. The detection of height jumps is based on a DBSCAN search within a custom search space. During the second stage, the building point sets are divided into sub-buildings in such a way that their roofs do not contain height jumps. The concept of sub-buildings without height jumps is introduced to break down the complex building models with height jumps into smaller parts, where shaping with half-spaces can be applied accurately. Finally, the sub-buildings are reconstructed separately with the corresponding half-spaces and then joined back together to form a complete building model. In the experiments, the methodology’s performance was demonstrated on a large scale and validated on an ISPRS benchmark dataset, where an RMSE of 0.29 m was obtained in terms of the height difference.
Ključne besede: LiDAR point cloud, building reconstruction, half-spaces, Boolean operations
Objavljeno v DKUM: 01.12.2023; Ogledov: 388; Prenosov: 25
Celotno besedilo (13,79 MB)
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Opis: V diplomskem delu je opisana implementacija mobilne aplikacije za operacijski sistem Android, ki na pametnem telefonu prikaže oblak točk svetlobnega zaznavanja in merjenja (ang. Light Detection and Ranging – LiDAR). Aplikacija omogoča premikanje po navideznem prostoru in pri vizualizaciji točk s pomočjo očal za navidezno resničnost (ang. Virtual Reality – VR) omogoča prepričljivejšo izkušnjo realnega okolja. Najcenejša očala so na trgu dostopna že za 15 €. S testiranjem aplikacije smo preverili njeno uporabnost in omejitve. Ugotovili smo, da se glede na zahtevnost pogleda pri upodabljanju večjih oblakov točk pojavijo večje razlike v hitrosti upodabljanja. V našem primeru smo pri merjenju števila sličic na sekundo (ang. frames per second – fps) pri oblaku z 1.000.000 točkami v osnovnem pogledu izmerili 54 fps, v pogledu VR pa 24 fps.
Ključne besede: LiDAR, oblak točk, očala, navidezna resničnost, pametni telefon
Objavljeno v DKUM: 21.09.2023; Ogledov: 503; Prenosov: 24
Celotno besedilo (7,85 MB)