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Found 5 papers, 1 papers with code
SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimation
Recent state-of-the-art optical flow estimation algorithms are two-frame based methods where optical flow is estimated sequentially for each consecutive image pair in a sequence.
Multi-Resolution Factor Graph Based Stereo Correspondence Algorithm
A dense depth-map of a scene at an arbitrary view orientation can be estimated from dense view correspondences among multiple lower-dimensional views of the scene.
A Novel Factor Graph-Based Optimization Technique for Stereo Correspondence Estimation
Dense disparities among multiple views is essential for estimating the 3D architecture of a scene based on the geometrical relationship among the scene and the views or cameras.
DDCNet-Multires: Effective Receptive Field Guided Multiresolution CNN for Dense Prediction
Dense optical flow estimation is challenging when there are large displacements in a scene with heterogeneous motion dynamics, occlusion, and scene homogeneity.
DDCNet: Deep Dilated Convolutional Neural Network for Dense Prediction
A sufficiently larger effective receptive field (ERF) and a higher resolution of spatial features within a network are essential for providing higher-resolution dense estimates.
