Results 41 to 50 of about 50,928 (258)
Case Study: Improving the Quality of Dairy Cow Reconstruction with a Deep Learning-Based Framework
Sensors, 2022 Three-dimensional point cloud generation systems from scanning data of a moving camera provide extra information about an object in addition to color. They give access to various prospective study fields for researchers.
Changgwon Dang +9 more
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TAILORED FEATURES FOR SEMANTIC SEGMENTATION WITH A DGCNN USING FREE TRAINING SAMPLES OF A COLORED AIRBORNE POINT CLOUD [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Automation of 3D LiDAR point cloud processing is expected to increase the production rate of many applications including automatic map generation.
E. Widyaningrum +5 more
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A survey of point cloud generation algorithms based on deep generative models
Dianzi Jishu YingyongAs a core task in the field of 3D vision, point cloud generation plays a vital role in scenarios such as point cloud shape completion, upsampling, and synthesis.
Lin Zhihao, Zhao Jiachi, Cheng Zhuo
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NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer
Sensors, 2022 Self-attention networks have revolutionized the field of natural language processing and have also made impressive progress in image analysis tasks. Corrnet3D proposes the idea of first obtaining the point cloud correspondence in point cloud registration.
Xiaobo Hu +4 more
doaj +1 more source
A Realism Metric for Generated LiDAR Point Clouds
International Journal of Computer Vision, 2022 AbstractA considerable amount of research is concerned with the generation of realistic sensor data. LiDAR point clouds are generated by complex simulations or learned generative models. The generated data is usually exploited to enable or improve downstream perception algorithms.
Larissa T. Triess +3 more
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Using Auto-ML on Synthetic Point Cloud Generation
Applied SciencesAutomated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper, we
Moritz Hottong +2 more
doaj +1 more source
3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
Applied Sciences, 2023 Building point cloud completion is the process of reconstructing missing parts of a building’s point cloud, which have been affected by external factors during data collection, to restore the original geometric shape of the building.
Bo Ye +6 more
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Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment
Molecular Oncology, EarlyView.This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley +1 more source
Journal of Asian Architecture and Building Engineering, 2022 Unmanned aerial vehicles (UAVs) are widely utilized in civil engineering due to the ease of use of UAVs in aerial monitoring tasks. A UAV-based mapping system is efficient and allows for a higher frequency of monitoring.
Daeyoon Moon +5 more
doaj +1 more source
A generic point error model for TLS derived point clouds
SPIE Proceedings, 2017 This work aims at developing a generic and anisotropic point error model, which is capable of computing magnitude and direction of a priori random errors, described in the form of error ellipsoids for each individual point of the cloud. The direct TLS observations are the range (ρ), vertical (α) and horizontal (θ) angles, each of which is in fact ...
Ozendi, Mustafa +2 more
openaire +3 more sources