Paper ID | TEC-5.1 | ||
Paper Title | ROBUST LIGHT FIELD SYNTHESIS FROM STEREO IMAGES WITH LEFT-RIGHT GEOMETRIC CONSISTENCY | ||
Authors | Chun-Hao Chao, Chang-Le Liu, Homer H. Chen, National Taiwan University, Taiwan | ||
Session | TEC-5: Image and Video Processing 1 | ||
Location | Area G | ||
Session Time: | Monday, 20 September, 13:30 - 15:00 | ||
Presentation Time: | Monday, 20 September, 13:30 - 15:00 | ||
Presentation | Poster | ||
Topic | Image and Video Processing: Formation and reconstruction | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | We propose a lightweight yet effective deep learning pipeline for light field synthesis from a single stereo image pair. Our pipeline consists of a convolutional network (CNN) that enforces a left-right consistency constraint on the light fields synthesized from left and right stereo views, a stage that merges light fields synthesized from left and right stereo views with a novel alpha blending technique, and a final refinement network using a unique 3D convolution operation. Our experiments quantitatively and qualitatively confirm the effectiveness and robustness of the proposed model, which performs favorably against state-of-the-art algorithms for light field synthesis from extremely sparse (only one, two, or four) views while using much fewer parameters. |