140 citations · 661 across the 41 of their papers we have counts for
7 papers · 1 filter
A Fusion Approach for Multi-Frame Optical Flow Estimation
Zhile Ren, Orazio Gallo, Deqing Sun +3
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has…
Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation
Deqing Sun, Xiaodong Yang, Ming-Yu Liu +1
We investigate two crucial and closely related aspects of CNNs for optical flow estimation: models and training. First, we design a compact but effective CNN model, called PWC-Net,…
Superpixel Sampling Networks
Varun Jampani, Deqing Sun, Ming-Yu Liu +2
Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks. Existing superpixel a…
Learning Dual Convolutional Neural Networks for Low-Level Vision
Jinshan Pan, Sifei Liu, Deqing Sun +8
In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and deha…
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
Anurag Ranjan, Varun Jampani, Lukas Balles +4
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation…
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli +3
Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is common…