3 research outputs found
Fast View Synthesis with Deep Stereo Vision
Novel view synthesis is an important problem in computer vision and graphics.
Over the years a large number of solutions have been put forward to solve the
problem. However, the large-baseline novel view synthesis problem is far from
being "solved". Recent works have attempted to use Convolutional Neural
Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of
learning scene geometry and interpreting camera motion, CNNs are often unable
to generate realistic novel views. In this paper, we present a novel view
synthesis approach based on stereo-vision and CNNs that decomposes the problem
into two sub-tasks: view dependent geometry estimation and texture inpainting.
Both tasks are structured prediction problems that could be effectively learned
with CNNs. Experiments on the KITTI Odometry dataset show that our approach is
more accurate and significantly faster than the current state-of-the-art. The
code and supplementary material will be publicly available. Results could be
found here https://youtu.be/5pzS9jc-5t