1. Prediction of the form of a hardened metal workpiece during the straightening processTadej Peršak, Jernej Hernavs, Tomaž Vuherer, Aleš Belšak, Simon Klančnik, 2023, izvirni znanstveni članek Opis: In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do. Ključne besede: sustraightening process, hardened workpiece, manufacturing, U-Net convolutional neural network, modeling, point cloud, digital sustainability Objavljeno v DKUM: 02.04.2024; Ogledov: 186; Prenosov: 16 Celotno besedilo (10,52 MB) Gradivo ima več datotek! Več... |
2. Novel Half-Spaces Based 3D Building Reconstruction Using Airborne LiDAR DataMarko Bizjak, Domen Mongus, Borut Žalik, Niko Lukač, 2023, izvirni znanstveni članek 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: 307; Prenosov: 19 Celotno besedilo (13,79 MB) Gradivo ima več datotek! Več... |
3. Reflection symmetry detection in earth observation dataDavid Podgorelec, Luka Lukač, Borut Žalik, 2023, izvirni znanstveni članek Opis: The paper presents a new algorithm for reflection symmetry detection, which is specialized to detect maximal symmetric patterns in an Earth observation (EO) dataset. First, we stress the particularities that make symmetry detection in EO data different from detection in other geometric sets. The EO data acquisition cannot provide exact pairs of symmetric elements and, therefore, the approximate symmetry must be addressed, which is accomplished by voxelization. Besides this, the EO data symmetric patterns in the top view usually contain the most useful information for further processing and, thus, it suffices to detect symmetries with vertical symmetry planes. The algorithm first extracts the so-called interesting voxels and then finds symmetric pairs of line segments, separately for each horizontal voxel slice. The results with the same symmetry plane are then merged, first in individual slices and then through all the slices. The detected maximal symmetric patterns represent the so-called partial symmetries, which can be further processed to identify global and local symmetries. LiDAR datasets of six urban and natural attractions in Slovenia of different scales and in different voxel resolutions were analyzed in this paper, demonstrating high detection speed and quality of solutions. Ključne besede: computer science, approximate symmetry, partial symmetry, local symmetry, point cloud, voxel, line segment Objavljeno v DKUM: 28.09.2023; Ogledov: 365; Prenosov: 13 Celotno besedilo (8,92 MB) Gradivo ima več datotek! Več... |
4. Simulating various terrestrial and UAV LiDAR scanning configurations for understory forest structure modellingMarina Hämmerle, Niko Lukač, K.-C. Chen, Zsófia Koma, C.-K. Wang, K. Anders, B. Höfle, 2017, objavljeni znanstveni prispevek na konferenci 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: 2156; Prenosov: 393 Celotno besedilo (3,71 MB) Gradivo ima več datotek! Več... |
5. Reconstructing 3D curves with euclidean minimal spanning treesSimon Kolmanič, Nikola Guid, 2006, izvirni znanstveni članek Opis: In this paper, we present a new efficient algorithm for reconstruction of nonintersecting 3D curves from a sufficiently den se sample. We use the Euclidean minimal spanning trees to identify line segments reconstructing curve shapes. To deal with more than one curve in a sample and to eliminate noisy data, we introduce chains of connected line segments. With the incremental growth based on heuristics, the chains contain finally curve shapes. The method is robust and fast for both 2D and 3D curves. Ključne besede: oblaki točk, rekonstrukcija krivulj, evklidska minimalna vpeta drevesa, point cloud, curve reconstruction, euclidean minimal spanning trees Objavljeno v DKUM: 10.07.2015; Ogledov: 2151; Prenosov: 42 Povezava na celotno besedilo |