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Found 8 papers, 1 papers with code
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation
Thus, we propose in this paper FlowFields++, where we combine the accurate matches of Flow Fields with a robust interpolation.
Fast Feature Extraction with CNNs with Pooling Layers
In recent years, many publications showed that convolutional neural network based features can have a superior performance to engineered features.
Fast View Synthesis with Deep Stereo Vision
Novel view synthesis is an important problem in computer vision and graphics.
Combining Stereo Disparity and Optical Flow for Basic Scene Flow
Scene flow is a description of real world motion in 3D that contains more information than optical flow.
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches.
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation
In this article we present a dense correspondence field approach that is much less outlier-prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields.
CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss
In this paper, we present a CNN based patch matching approach for optical flow estimation.
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation
In this paper we present a dense correspondence field approach that is much less outlier prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields.
