Results 31 to 40 of about 7,734,831 (297)
Hypernetwork approach to generating point clouds
CoRR, 2020 In this work, we propose a novel method for generating 3D point clouds that leverage properties of hyper networks. Contrary to the existing methods that learn only the representation of a 3D object, our approach simultaneously finds a representation of the object and its 3D surface.
Przemyslaw Spurek +5 more
openaire +4 more sources
3D point cloud generation reconstruction from single image based on image retrieval
Results in Optics, 2021 Most of the 3D point cloud generation methods from a single image are only applicable to a single target. However, the real image often contains multiple targets, which is difficult to generate 3D point clouds from the network directly.
Hui Chen, Yipeng Zuo, Yong Tong, Li Zhu
doaj +1 more source
Attention-Based Dense Point Cloud Reconstruction From a Single Image
IEEE Access, 2019 Three-dimensional Reconstruction has drawn much attention in computer vision. Generating a dense point cloud from a single image is a more challenging task.
Qiang Lu +4 more
doaj +1 more source
Accuracy assessment of building point clouds automatically generated from iphone images [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images
B. Sirmacek, R. Lindenbergh
doaj +1 more source
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
openaire +4 more sources
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
openaire +2 more sources
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
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
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
doaj +1 more source
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
doaj +1 more source