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: The increasing adoption of 2D/3D laser line sensors in industrial and research applications necessitates accurate and efficient simulation tools for tasks such as surface inspection, dimensional verification, and quality control. This paper presents a novel algorithm developed in MATLAB for simulating the measurements of any 2D/3D laser line sensor on STL CAD models. The algorithm uses a modified fast-ray triangular intersection method, addressing challenges such as overlapping triangles in assembly models and incorporating sensor resolution to ensure realistic simulations. Quantitative analysis shows a significant reduction in computation time, enhancing the practical utility of the algorithm. The simulation results exhibit a mean deviation of 0.42 mm when compared to real-world measurements. Notably, the algorithm effectively handles complex geometric features, such as holes and grooves, and offers flexibility in generating point cloud data in both local and global coordinate systems. This work not only reduces the need for physical prototyping, thereby contributing to sustainability, but also supports AI training by generating accurate synthetic data. Future work should aim to further optimize the simulation speed and explore noise modeling to enhance the realism of simulated measurements.
Ključne besede: 2D/3D laser line sensor, profilometry, simulation, point cloud, measurement generation, STL, Matlab
Objavljeno v DKUM: 10.01.2025; Ogledov: 0; Prenosov: 10
Celotno besedilo (8,99 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: Information about the 3D structure of understory vegetation is of high relevance in forestry research and management (e.g., for complete biomass estimations). However, it has been hardly investigated systematically with state-of-the-art methods such as static terrestrial laser scanning (TLS) or laser scanning from unmanned aerial vehicle platforms (ULS). A prominent challenge for scanning forests is posed by occlusion, calling for proper TLS scan position or ULS flight line configurations in order to achieve an accurate representation of understory vegetation. The aim of our study is to examine the effect of TLS or ULS scanning strategies on (1) the height of individual understory trees and (2) understory canopy height raster models. We simulate full-waveform TLS and ULS point clouds of a virtual forest plot captured from various combinations of max. 12 TLS scan positions or 3 ULS flight lines. The accuracy of the respective datasets is evaluated with reference values given by the virtually scanned 3D triangle mesh tree models. TLS tree height underestimations range up to 1.84 m (15.30 % of tree height) for single TLS scan positions, but combining three scan positions reduces the underestimation to maximum 0.31 m (2.41 %). Combining ULS flight lines also results in improved tree height representation, with a maximum underestimation of 0.24 m (2.15 %). The presented simulation approach offers a complementary source of information for efficient planning of field campaigns aiming at understory vegetation modelling.
Ključne besede: forest structure, understory, laser scanning simulation, full waveform, 3D point cloud analysis, field campaign planning
Objavljeno v DKUM: 09.10.2017; Ogledov: 2247; Prenosov: 406
Celotno besedilo (3,71 MB)
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