In this paper, we first demonstrate that softmax loss will produce a lower bound and cause a convergence problem if we directly perform feature comparison by ...

Aug 1, 2022 · Discriminative feature abstraction is indispensable for boosting the overall performance of learning models. For 3D data, in both point cloud ...

Discriminative feature abstraction is indispensable for boosting the overall performance of learning models. For 3D data, in both point cloud and mesh models, ...

... hypersphere embedding for 3D mesh CNNs. https://doi.org/10.1016/j.ins.2022.05.104 ·. Journal: Information Sciences, 2022, p. 1158-1173. Publisher: Elsevier BV.

Co-authors ; Discriminative Feature Abstraction by Deep L2 Hypersphere Embedding for 3D Mesh CNNs. MK Afzal, JM Adam, HMR Afzal, Y Zang, SA Bello, C Wang, J Li.

Discriminative feature abstraction by deep L2 hypersphere embedding for 3D mesh CNNs. ... 3D Face Reconstruction From Single 2D Image Using Distinctive Features.

Discriminative feature abstraction by deep L2 hypersphere embedding for 3D mesh CNNs. Inf. Sci. 607: 1158-1173 (2022). [j2]. view. electronic edition via DOI ...

FFPointNet: Local and global fused feature for 3D point clouds analysis ... Discriminative feature abstraction by deep L2 hypersphere embedding for 3D mesh CNNs.

... Discriminative feature abstraction by deep L2 hypersphere embedding for 3D mesh CNNs. Inf. Sci. 607: 1158-1173 (2022) 6. Naftaly Wambugu, Yiping Chen ...

Readers: Everyone. Discriminative feature abstraction by deep L2 hypersphere embedding for 3D mesh CNNs ... model generation from MLS point cloud and 3D mesh ...