Results 31 to 40 of about 50,928 (258)
Learning to Generate Realistic LiDAR Point Clouds
, 2022 We present LiDARGen, a novel, effective, and controllable generative model that produces realistic LiDAR point cloud sensory readings. Our method leverages the powerful score-matching energy-based model and formulates the point cloud generation process as a stochastic denoising process in the equirectangular view. This model allows us to sample diverse
Vlas Zyrianov, Xiyue Zhu, Shenlong Wang
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3D‐FEGNet: A feature enhanced point cloud generation network from a single image
IET Computer Vision, 2023 Deep learning‐based single view 3D reconstruction is a hot topic in computer vision. However, predicting a more realistic 3D point cloud from a single image is an ill‐posed problem.
Ende Wang +4 more
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Fast Point Cloud Generation with Straight Flows
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D real-world.
Lemeng Wu +8 more
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ON GEOMETRIC PROCESSING OF MULTI-TEMPORAL IMAGE DATA COLLECTED BY LIGHT UAV SYSTEMS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Data collection under highly variable weather and illumination conditions around the year will be necessary in many applications of UAV imaging systems. This is a new feature in rigorous photogrammetric and remote sensing processing.
T. Rosnell, E. Honkavaara, K. Nurminen
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RPG: Learning Recursive Point Cloud Generation
CoRR, 2021 In this paper we propose a novel point cloud generator that is able to reconstruct and generate 3D point clouds composed of semantic parts. Given a latent representation of the target 3D model, the generation starts from a single point and gets expanded recursively to produce the high-resolution point cloud via a sequence of point expansion stages ...
Wei-Jan Ko +5 more
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THE FEASIBILITY OF 3D POINT CLOUD GENERATION FROM SMARTPHONES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones.
N. Alsubaie, N. El-Sheimy
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AUTOMATIC EXTRACTION OF BUILDING OUTLINE FROM HIGH RESOLUTION AERIAL IMAGERY [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately.
Y. Wang
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GENERATION OF 3-D LARGE-SCALE MAPS USING LIDAR POINT CLOUD DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 3-Dimensional geospatial data is essential for creating and utilizing real-world visualizations for analyzing infrastructure design improvements. However, techniques exist such as Total Station, Global Positioning System (GPS), and Google Earth data ...
L. Dhruwa, P. K. Garg
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Generating Unrestricted 3D Adversarial Point Clouds
CoRR, 2021 Utilizing 3D point cloud data has become an urgent need for the deployment of artificial intelligence in many areas like facial recognition and self-driving. However, deep learning for 3D point clouds is still vulnerable to adversarial attacks, e.g., iterative attacks, point transformation attacks, and generative attacks. These attacks need to restrict
Xuelong Dai +3 more
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Point Cloud Generation from sUAS-Mounted iPhone Imagery: Performance Analysis [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 The rapidly growing use of sUAS technology and fast sensor developments continuously inspire mapping professionals to experiment with low-cost airborne systems. Smartphones has all the sensors used in modern airborne surveying systems, including GPS, IMU,
A. D. Ladai, J. Miller
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