Fast View Synthesis with Deep Stereo Vision
This addresses the challenge of generating realistic novel views in computer vision and graphics, offering an incremental improvement over existing CNN-based methods.
The paper tackles the large-baseline novel view synthesis problem by proposing a method that decomposes it into geometry estimation and texture inpainting using CNNs, achieving more accurate and significantly faster results than state-of-the-art on the KITTI Odometry dataset.
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-5t0