March 8, 2017
Currently, the vision community focuses on extrinsic representation of 3D shapes suitable for learning rigid shapes, whereas the geometry community focuses on adapting convolutional neural networks (CNNs) to non-Euclidean manifolds using intrinsic shape properties for creating optimal descriptors. A method to unify these two complementary approaches has remained elusive. CNN architectures operate on planar structures, which is a challenge for 3D model objects; however, connectivity between different surfaces is not preserved, which is vital for holistic shape analysis.
Researchers at Purdue University have developed the means of transferring 2D images and recreating 3D forms. By learning shapes of 3D images, the developed program can classify and recreate these images for 3D modelling used for differential geometry or shape analysis. This shape recovery is used with the aid of CNN architecture, which allows for fast computing, efficient learning, and sharp mesh creation, allowing for an easier transition from design to 3D form for efficient forensic science and greater special analysis for virtual reality technology.
由于技术保密工作限制，技术信息无法完全展现，请通过邮箱或短信联系我们，获取更多技术资料。
adapting
efficient
shape
cnns
shapes
learning
geometry
cnn
analysis
images
B5, No.1600, North Guoquan Rd, Yangpu, Shanghai
info@yintrust.com
021-65679356
Premium