Computing Range Flow from Multi-modal Kinect Data

By Jens-Malte Gottfried, Janis Fehr, Christoph S. Garbe. 2011

65 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Wanqing Li
2. Pichao Wang
3. Philip O. Ogunbona
4. Zhimin Gao
5. Chang-Fu Tang
6. Luc Van Gool
7. Yuyao Zhang
8. Deqing Sun

In Media Technology

1. Hanspeter Pfister
2. Chang-Fu Tang
3. Wanqing Li
4. Daniel Cremers
5. Philip O. Ogunbona
6. Erik B. Sudderth
7. Yuyao Zhang
8. Zhimin Gao

Citations in the corpus

• Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “With the advent of affordable RGB-D cameras, scene flow methods for RGB-D data have also been proposed [12, 31, 15, 16, 55, 39, 18, 51, 45].”
• A primal-dual framework for real-time dense RGB-D scene flow (2015, methodology): “In this subsection we compare our approach (named PD flow) with a recent work (RGB-D flow) [11] in terms of accuracy and temporal performance.”
• Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications (2012, background): “Kinect have attracted the attention of researchers from other fields [3–11] including mapping and 3D modeling [12–15].”
• Layered RGBD scene flow estimation (2015, methodology): “[15] propose the first RGBD scene flow method for Kinect data by extending the optical flow formulation of Horn and Schunck [19].”