We propose a novel end-to-end supervised convolutional neural network(CNN) to compute disparity from a pair of stereo images. To solve the current problem of computing the high-quality disparity in ill-areas, our cascade spatial pyramid pooling (CSPP) substructure is able to gather global context information by aggregating the context information in different positions and different feature block scales from coarse to fine. We also introduce a warp layer, the right feature map is warped with the previously predicted disparity, and then is compared with the left feature map to form a cost volume. We learn the disparity from the cost volume with different level features information. We evaluate our method on three stereo datasets, and results show our method has advantages in textured areas, target edge areas and efficiency. We also achieve a high ranking performance.
As the cGANs achieves great success on pix to pix problem [12], we proposed a new architecture based on cGAN to solve our optical flow estimation problem. Specifically, we propose a loss function which consists of an adversarial loss and a content loss. The adversarial loss is the pixel-to-pixel loss. We use a discriminator network which is trained to differentiate the ground-truth flow and the generated flow on pixel space. The content loss focuses on perceptual similarity of the ground-truth flow and the generated flow. Our architecture (FlowGan) contains a generator based on FlowNetS with Dense Block to make it deeper and a Markovian discriminator to classify image patch instead of the whole image. We train our network with FlyingChairs datasets and evaluated our network on MPISintel. FlowGan can get competitive results with practical speed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.