Tsinghua University

Hung-Jin Lin

Shang-Hong Lai

Ching-Wei Tseng

A method of image completion comprises: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types; inputting the training image corresponding to the plurality of objective types such that the plurality of conditional generative adversarial networks respectively conduct a corruption feature training; inputting the image in need of repair and respectively conducting an image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images; and judging a reasonable probability of the plurality of repaired images through a probability analyzer, choosing an accomplished image and outputting the accomplished image through an output interface.

Method of image completion

An indoor scene structural estimation system and an estimation method based on deep learning network are provided. An indoor scene structural estimation system based on deep learning network includes a 2D encoder, a 2D plane decoder, a 2D edge decoder, a 2D corner decoder, and a 3D encoder. The 2D encoder receives an input image and encodes the input image. The 2D plane decoder is connected to the 2D encoder, decodes the encoded input image, and generates a 2D plane segment layout image. The 2D plane decoder is connected to the 2D encoder, decodes the encoded input image, and generates a 2D plane segment layout image. The 2D corner decoder is connected to 2D encoder, decodes the encoded input image, and generates a 2D corner layout image.

Growing community of inventors

Taichung, Taiwan

Hung-Jin Lin