Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Ziyin Zeng"'
Autor:
Xian Li, Beilei Shi, Jian-Wen Huang, Ziyin Zeng, Yu Yang, Lilan Zhang, Jian Min, Chun-Chi Chen, Rey-Ting Guo
Publikováno v:
Bioresources and Bioprocessing, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract Using enzymes to hydrolyze and recycle poly(ethylene terephthalate) (PET) is an attractive eco-friendly approach to manage the ever-increasing PET wastes, while one major challenge to realize the commercial application of enzyme-based PET de
Externí odkaz:
https://doaj.org/article/864c977c37264bf199739ae3bf3488a2
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 9051-9066 (2023)
3-D point cloud semantic segmentation is a fundamental task for scene understanding, but this task remains challenging due to the diverse scene classes, data defects, and occlusions. Most existing deep learning-based methods focus on new designs of f
Externí odkaz:
https://doaj.org/article/50990850919f447682e23ed1a5c7634c
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 119, Iss , Pp 103285- (2023)
3D point cloud semantic segmentation is crucial for 3D environment perception and scene understanding, where learning of local context in point clouds is a crucial challenge. Existing approaches typically explore local context based on the predefined
Externí odkaz:
https://doaj.org/article/d47facd763404d46b63257b5f8632f86
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 112, Iss , Pp 102953- (2022)
Given the prominence of 3D sensors in recent years, 3D point cloud scene data are worthy to be further investigated. Point cloud scene understanding is a challenging task because of its characteristics of large-scale and discrete. In this study, we p
Externí odkaz:
https://doaj.org/article/ba0fe9954b5d449da056a0be0e7196a1
Publikováno v:
Remote Sensing, Vol 15, Iss 1, p 131 (2022)
Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constrai
Externí odkaz:
https://doaj.org/article/852b1bf57b094cafa134ccce089247ea
Publikováno v:
Remote Sensing, Vol 14, Iss 16, p 4055 (2022)
Point cloud semantic segmentation, a challenging task in 3D data processing, is popular in many realistic applications. Currently, deep learning methods are gradually being applied to point cloud semantic segmentation. However, as it is difficult to
Externí odkaz:
https://doaj.org/article/8872b1cabcb244c088b83ffab81fb181
Publikováno v:
Remote Sensing, Vol 13, Iss 23, p 4917 (2021)
Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud comple
Externí odkaz:
https://doaj.org/article/58e500052cdc4813bfea607a381de9f5
Publikováno v:
Remote Sensing, Vol 13, Iss 17, p 3484 (2021)
Feature extraction on point clouds is an essential task when analyzing and processing point clouds of 3D scenes. However, there still remains a challenge to adequately exploit local fine-grained features on point cloud data due to its irregular and u
Externí odkaz:
https://doaj.org/article/e929d26aba7a4a85958769910946a7f2
Publikováno v:
Remote Sensing, Vol 13, Iss 3484, p 3484 (2021)
Remote Sensing; Volume 13; Issue 17; Pages: 3484
Remote Sensing; Volume 13; Issue 17; Pages: 3484
Feature extraction on point clouds is an essential task when analyzing and processing point clouds of 3D scenes. However, there still remains a challenge to adequately exploit local fine-grained features on point cloud data due to its irregular and u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9d77ada0ba6379f3312aabd92c845e8
https://www.mdpi.com/2072-4292/13/17/3484
https://www.mdpi.com/2072-4292/13/17/3484