Research on Spatial Transformation in Image Based on Deep Learning
In the field of computer graphics,synthesizing a new view of 3D objects in images from a single perspective image is an important problem.A part of the object is unobservable,since 3D objects mapping to image space will result in partial occlusion or self-occlusion of objects.The synthesis needs to infer spatial structure and posture of the object.The uncertainty due to occlusion is a problem in the synthesis.In this paper,the problem is solved by establishing a convolutional neural network(CNN),which uses images including multiple chairs as dataset.First of all,we study related networks to propose a novel multi-parallel and multi-level encoding-decoding network,which implements the transformation from a single perspective image and angle semantic information to a new perspective synthetic image in an end-to-end way.Secondly,the network is trained by establishing a dataset.Finally,it is proved the neural network performs better edge smoothing effect and higher precision in image synthesis than state-of-the-art networks.
image space spatial transformation,semantic information,image synthesis
Peng Gao Qingxuan Jia
Laboratory of Space Robot,Beijing University of Posts and Telecommunications,No.10 Xitucheng Road,Haidian District,Beijing,China
国际会议
西安
英文
471-480
2019-01-19（万方平台首次上网日期，不代表论文的发表时间）