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PCT: Point cloud transformer [PDF]
Computational Visual Media, 2021 The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image ...
Meng-Hao Guo +5 more
openaire +7 more sources
AIRBORNE LIDAR POINT CLOUD CLASSIFICATION FUSION WITH DIM POINT CLOUD [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Airborne Light Detection And Ranging (LiDAR) point clouds and images data fusion have been widely studied. However, with recent developments in photogrammetric technology, images can now provide dense image matching (DIM) point clouds.
M. Zhou, Z. Kang, Z. Wang, M. Kong
doaj +3 more sources
Cloud Point Behavior of Poly(trifluoroethyl methacrylate) in Supercritical CO2–Toluene Mixtures [PDF]
MoleculesSupercritical CO2 (scCO2) is a versatile solvent for polymer processing; however, many partially fluorinated polymers exhibit limited solubility in neat scCO2. Organic cosolvents such as toluene can enhance polymer–solvent interactions, thereby improving
James R. Zelaya, Gary C. Tepper
doaj +2 more sources
Adaptive Clustering for Point Cloud
SensorsThe point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to ...
Zitao Lin +6 more
doaj +3 more sources
Cloud point extraction coupled with back extraction: a green methodology in analytical chemistry. [PDF]
Forensic Sci Res, 2019 Recently, cloud point extraction (CPE) coupled with back extraction (BE) has been suggested as a promising alternative to liquid-liquid extraction. In CPE, non-ionic surfactants in aqueous solutions form micelles and the solution becomes turbid when ...
Kori S.
europepmc +2 more sources
Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests [PDF]
Remote Sensing, 2017 This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF ...
Huan Ni, Xiangguo Lin, Jixian Zhang
doaj +3 more sources
Novel, energy efficient and green cloud point extraction: technology and applications in food processing. [PDF]
J Food Sci Technol, 2019 Arya SS +4 more
europepmc +2 more sources
Masked Autoencoders for Point Cloud Self-supervised Learning [PDF]
European Conference on Computer Vision, 2022 As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning,
Yatian Pang +5 more
semanticscholar +1 more source
Geometric Transformer for Fast and Robust Point Cloud Registration [PDF]
Computer Vision and Pattern Recognition, 2022 We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios, showing great potential in registration. They
Zheng Qin +5 more
semanticscholar +1 more source
Stratified Transformer for 3D Point Cloud Segmentation [PDF]
Computer Vision and Pattern Recognition, 2022 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies.
Xin Lai +7 more
semanticscholar +1 more source