• Publications
  • Influence
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
TLDR
PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume, and outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks.
Secrets of optical flow estimation and their principles
TLDR
It is discovered that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques, and while median filtering of intermediate flow fields during optimization is a key to recent performance gains, it leads to higher energy solutions.
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
TLDR
This work proposes an end-to-end convolutional neural network for variable-length multi-frame video interpolation, where the motion interpretation and occlusion reasoning are jointly modeled.
Blind Image Deblurring Using Dark Channel Prior
TLDR
This work introduces a linear approximation of the min operator to compute the dark channel and achieves state-of-the-art results on deblurring natural images and compares favorably methods that are well-engineered for specific scenarios.
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
TLDR
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
On Bayesian Adaptive Video Super Resolution
  • Ce Liu, Deqing Sun
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine…
  • 1 February 2014
TLDR
This paper proposes a Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel, and noise level while reconstructing the original high-resolution frames and confirms empirical observations that an intermediate size blur kernel achieves the optimal image reconstruction results.
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them
TLDR
It is discovered that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques, and a new objective function is derived that formalizes the median filtering heuristic and develops a method that can better preserve motion details.
A Bayesian approach to adaptive video super resolution
TLDR
A Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel and noise level while reconstructing the original high-res frames is proposed.
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
TLDR
A network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice that outperforms existing state-of-the-art techniques on 3D segmentation tasks.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
TLDR
This work addresses the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions with Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems.
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