Results 21 to 30 of about 5,267,329 (301)
Analytical Point-Cloud Based Geometric Modeling for Additive Manufacturing and Its Application to Cultural Heritage Preservation [PDF]
, 2018 Point-cloud is a valuable piece of information for geometric modeling and additive manufacturing of different types of objects. In most cases, a point-cloud is obtained by using the 3D scanners or by using image processing. Alternatively, one can rely on
A.M.M. Sharif , Ullah +3 more
core +1 more source
Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds
Remote Sensing, 2021 Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity.
Shiming Li +4 more
doaj +1 more source
Point cloud geometry compression using neural implicit representations [PDF]
, 2023 openIn recent years, the increasing prominence of 3D point clouds in various applications has led to an escalating need for efficient storage and transmission methods.
MOTAMENI, AMIRHOSSEIN
core
End-to-End Point Cloud Completion Network with Attention Mechanism
Sensors, 2022 We propose a conceptually simple, general framework and end-to-end approach to point cloud completion, entitled PCA-Net. This approach differs from the existing methods in that it does not require a “simple” network, such as multilayer perceptrons (MLPs),
Yaqin Li +4 more
doaj +1 more source
CoRR, 2018 Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data.
Chun-Liang Li +4 more
openaire +3 more sources
DPDist: Comparing Point Clouds Using Deep Point Cloud Distance [PDF]
, 2020 We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is sampled. The surface is estimated locally and efficiently using the 3D modified Fisher vector representation.
Dahlia Urbach +2 more
openaire +2 more sources
VIPDA: A Visually Driven Point Cloud Denoising Algorithm Based on Anisotropic Point Cloud Filtering
Frontiers in Signal Processing, 2022 Point clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have a growing interest in recent applications, such as e-health or autonomous means of transport.
Tiziana Cattai +3 more
doaj +1 more source
Multistage Adaptive Point-Growth Network for Dense Point Cloud Completion
Remote Sensing, 2022 The point cloud data from actual measurements are often sparse and incomplete, making it difficult to apply them directly to visual processing and 3D reconstruction.
Ruidong Hao +6 more
doaj +1 more source
Predictive point-cloud compression [PDF]
ACM SIGGRAPH 2005 Sketches on - SIGGRAPH '05, 2005 Point clouds have recently become a popular alternative to polygonal meshes for representing three-dimensional geometric models. 3D photography and scanning systems acquire the geometry and appearance of real-world objects in form of point samples.
Gumhold, S. +3 more
openaire +3 more sources
Remote Sensing, 2020 Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper
Jian Chen +4 more
doaj +1 more source