A primal-dual framework for real-time dense RGB-D scene flow

By Mariano Jaimez, Mohamed Souiai, Javier González Jiménez, Daniel Cremers. 2015

125 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

1. Alexandre Alahi
2. Li Fei-Fei

In Computer Vision and Pattern Recognition

1. Wanqing Li
2. Pichao Wang
3. Philip O. Ogunbona
4. Zhimin Gao
5. Chang-Fu Tang
6. G. Wang
7. Jun Liu
8. Li Fei-Fei

Citations in the corpus

• Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “1 we briefly present the primal-dual framework for real-time dense RGB-D scene flow computation (hereafter denoted by PD-flow [11]).”
• Unsupervised Learning of Long-Term Motion Dynamics for Videos (2017, methodology): “We augment the RGB videos with Depth modality and estimate the 3D flows [6].”
• FlowNet3D: Learning Scene Flow in 3D Point Clouds (2018, unknown): “With the recent advent of commodity depth sensors, it has become feasible to estimate scene flow from monocular RGB-D images [9], by generalizing variational 2D flow algorithms to 3D [10, 14] and exploiting more geometric cues provided by the depth channel [21, 11, 23].”