Results 11 to 20 of about 6,825,217 (303)
DENOISING OF 3D POINT CLOUDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 A method to remove random errors from 3D point clouds is proposed. It is based on the estimation of a local geometric descriptor of each point. For mobile mapping LiDAR and airborne LiDAR, a combined standard mesurement uncertainty of the LiDAR system ...
E. Mugner, N. Seube
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Unsupervised Domain Adaptation for 3D Point Clouds by Searched Transformations
IEEE Access, 2022 Input-level domain adaptation reduces the burden of a neural encoder without supervision by reducing the domain gap at the input level. Input-level domain adaptation is widely employed in 2D visual domain, e.g., images and videos, but is not utilized for
Dongmin Kang +3 more
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3D Point Cloud Compression [PDF]
The 24th International Conference on 3D Web Technology, 2019 In recent years, 3D point clouds have enjoyed a great popularity for representing both static and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of providing a simpler, denser and more close-to-reality representation. However, point clouds always carry a huge amount of data.
Chao Cao, Marius Preda, Titus Zaharia
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Equivariant Point Network for 3D Point Cloud Analysis [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Features that are equivariant to a larger group of symmetries have been shown to be more discriminative and powerful in recent studies [4], [40], [5]. However, higher-order equivariant features often come with an exponentially-growing computational cost. Furthermore, it remains relatively less explored how rotation-equivariant features can be leveraged
Haiwei Chen +4 more
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GEOMETRIC FEATURES INTERPRETATION OF PHOTOGRAMMETRIC POINT CLOUD FROM UNMANNED AERIAL VEHICLE [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Recent days point clouds have become one of the most common 3D sources of information which is provides accurate geometry features of the object. 3D point clouds can be derived from either photogrammetry, Lidar or SAR in some cases depending upon the ...
H. Harshit, S. K. P. Kushwaha, K. Jain
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EXTRACTION AND SHAPE RECONSTRUCTION OF GUARDRAILS USING MOBILE MAPPING DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 The mobile mapping system (MMS) can acquire dense point-clouds of roads and roadside features. Roads are often separated into roadways and walkways in many urban areas. Since guardrails are installed to separate roadways and sidewalks, it is important to
H. Matsumoto, Y. Mori, H. Masuda
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Generative Models for 3D Point Clouds
CoRR, 2023 Point clouds are rich geometric data structures, where their three dimensional structure offers an excellent domain for understanding the representation learning and generative modeling in 3D space. In this work, we aim to improve the performance of point cloud latent-space generative models by experimenting with transformer encoders, latent-space flow
Lingjie Kong +2 more
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Nihon Kikai Gakkai ronbunshu, 2020 Localization in autonomous vehicles is an important technology, and the use of 3D point clouds, which provide accurate information on the road surroundings, has been attracting attention to help improve localization.
Takaya MURAKAMI +4 more
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Remote Sensing, 2021 Large-scale 3D-scanned point clouds enable the accurate and easy recording of complex 3D objects in the real world. The acquired point clouds often describe both the surficial and internal 3D structure of the scanned objects.
Weite Li +4 more
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A digital hologram-based encryption and compression method for 3D models
Frontiers in Physics, 2022 This study proposes a novel method to compress and decompress the 3D models for safe transmission and storage. The 3D models are first extracted to become 3D point clouds, which would be classified by the K-means algorithm.
Yukai Sun +7 more
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