Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Shivanand Venkanna Sheshappanavar"'
Publikováno v:
Image Analysis ISBN: 9783031314346
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d3e9f58d557dfb94fe40b3a2de81545
https://doi.org/10.1007/978-3-031-31435-3_26
https://doi.org/10.1007/978-3-031-31435-3_26
Publikováno v:
ACM Multimedia
Polygon meshes are a popular representation in computer graphics. They efficiently provide delineations of complex 3D shapes. However, their irregular structure hinders mesh analysis efforts in deep learning frameworks; few neural networks exist to d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4822289df02411e14a394fdb4c2dde6e
https://doi.org/10.1145/3474085.3475468
https://doi.org/10.1145/3474085.3475468
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::18cc479c27e857e55ab0b4634dae039e
https://doi.org/10.1109/iccvw54120.2021.00240
https://doi.org/10.1109/iccvw54120.2021.00240
Publikováno v:
2021 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba458585bf2e198148f10f8d8441e845
https://doi.org/10.1109/icip42928.2021.9506311
https://doi.org/10.1109/icip42928.2021.9506311
Publikováno v:
MIPR
With the advent of PointNet, the popularity of deep neural networks has increased in point cloud analysis. PointNet’s successor, PointNet++, partitions the input point cloud and recursively applies PointNet to capture local geometry. PointNet++ mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2fd06bad5c811beb8f6b2f9eabe316ce
https://doi.org/10.1109/mipr51284.2021.00031
https://doi.org/10.1109/mipr51284.2021.00031
Publikováno v:
CVPR Workshops
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::021a7d381e4e3f40edf0abae3c99cf3e
https://doi.org/10.1109/cvprw50498.2020.00139
https://doi.org/10.1109/cvprw50498.2020.00139