Kinecting the dots: Particle based scene flow from depth sensors

By Simon Hadfield, Richard Bowden. 2011

110 citations recorded by the source corpus.

Subfields: Aerospace Engineering, 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 Aerospace Engineering

1. Mariano Jaimez
2. Jiang Wang
3. Wanqing Li
4. Daniel Cremers
5. Xiaochun Cao
6. Philip O. Ogunbona
7. Mohamed Souiai
8. Huan Fu

In Computer Vision and Pattern Recognition

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

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].”
• 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].”
• Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold (2014, background): “and estimation [3], scene flow estimation [4], hand gesture recognition [5], and face super-resolution [6].”
• Depth Enhanced Saliency Detection Method (2014, background): “Depth information has been demonstrated useful on many applications [11, 7, 10].”
• Robust 3D Action Recognition with Random Occupancy Patterns (2012, background): “1 Introduction Recently, the advance of the imaging technology has enabled us to capture the depth information in real-time, and various promising applications have been proposed [1–4].”
• A primal-dual framework for real-time dense RGB-D scene flow (2015, background): “[15] proposed a particle filter to estimate the motion field from depth and intensity images coming from a RGBD camera, and extended this work in [16] to operate with any combination of photometric and range sensors.”
• Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses (2013, background): “These new acquisition devices have stimulated the development of various promising applications, including human pose reconstruction and estimation [1, 6, 12], scene flow estimation [7], hand gesture recognition [2], face super-resolution [3].”