Search Results for author:
Found 7 papers, 5 papers with code
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching
A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios.
Explicit Motion Disentangling for Efficient Optical Flow Estimation
In this paper, we propose a novel framework for optical flow estimation that achieves a good balance between performance and efficiency.
RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
Specifically, we first estimate optical flow between a pair of video frames, and then synthesize a new image from this pair based on the predicted flow.
DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow
Recently, the dense correlation volume method achieves state-of-the-art performance in optical flow.
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress.
Practical Wide-Angle Portraits Correction with Deep Structured Models
Wide-angle portraits often enjoy expanded views.
Disentangled Image Matting
Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.
