3D Object Detection with Multiple Kinects

By Wandi Susanto, Marcus Rohrbach, Bernt Schiele. 2012

41 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. Ling Shao
2. Michael Firman
3. Huijing Zhao
4. Jungong Han
5. Quanshi Zhang
6. Ziyun Cai
7. Li Liu
8. Yi-Zhe Song

In Computer Vision and Pattern Recognition

1. Quanshi Zhang
2. Li Liu
3. Jungong Han
4. Ling Shao
5. Song-Chun Zhu
6. Michael Firman
7. Xiaowei Shao
8. Huijing Zhao

Citations in the corpus

• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The curves under the images are the accelerometer data at 50 Hz of devices attached to the knife, the mixing spoon, the small spoon, the peeler, the glass, the oil bottle, and the pepper dispenser mentioned datasets above, another 26 extra RGB-D datasets for different applications are also added int”
• Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models (2013, background): “Recently, RGBD images made object detection much easier [14, 15, 16], and even the structure discovery [17] or segmentation of indoor environments [18, 19] produced object-level results.”
• RGBD Datasets: Past, Present and Future (2016, methodology): “MPII Multi-Kinect dataset [99] 2240 frames total from 4 Kinects ## Polygon segmentation of objects arranged on kitchen worktop ’12”