Nov 27, 2019 · We design an end-to-end network for 3D shape analysis that combines pointwise low-level geometric and high-level semantic information.
... The point cloud, a widely used form of 3D representation, is a set of points with 3D coordinates sampled from the surface of a 3D object or scenario [40] .
An end-to-end network is designed for 3D shape analysis, which combines pointwise low-level geometric and high-level semantic information.
Pointwise geometric and semantic learning network on 3D point clouds. https://doi.org/10.3233/ica-190608. Journal: Integrated Computer-Aided Engineering, 2019 ...
Pointwise geometric and semantic learning network on 3D point clouds. D. Zhang, F. He, Z. Tu, L. Zou, and Y. Chen. Integr. Comput. Aided Eng., 27 (1): 57-75 ...
Point cloud, an efficient 3D object representation, has become popular with the development of depth sensing and 3D laser scanning techniques.
Pointwise geometric and semantic learning network on 3D point clouds. Integr. Comput. Aided Eng. Pub Date : 2019-11-27. DOI : 10.3233/ica-190608. Dejun Zhang 1 ...
In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is ...
PointGS establishes a mutual supervision mechanism that can bridge the two spaces and fuse complementary information for better analyzing 3D point cloud data.
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A deep learning framework to learn point-wise description from a set of shapes without supervision that leverages self-supervision to define a relevant loss ...