hzhongresearch/quickfind_mask_data

QuickFind mask data

QuickFind is a fast segmentation and object detection algorithm using only depth maps. Depth maps are images captured from depth sensors like Kinect. The idea is in the future depth sensors will be common so such an algorithm will be useful. This project was created during my PhD. The associated research paper was presented at PerCom Workshops 2016.

The data contains amended ground truth of the RGB-D Scenes dataset used in the QuickFind paper. The ground truth in the original RGB-D scenes dataset had many errors so I manually traced out the location of each object. Credit for RGB-D Scenes goes to Lai et al. Please visit their site and credit them if you use my amended data in conjunction with their RGB-D Scenes dataset.

This is the dataset for QuickFind.

Description of data

Each file is 24 bit, 3 channel in PNG format.

The RGB values for each object class.

Object class	Object instance	R	G	B
Bowl	bowl_2	255	0	0
Bowl	bowl_3	255	255	0
Bowl	bowl_4	0	255	0
Cap	cap_1	0	255	255
Cap	cap_3	0	0	255
Cap	cap_4	255	0	255
Cereal	cereal_1	192	0	0
Cereal	cereal_2	192	192	0
Cereal	cereal_4	0	192	0
Coffee	coffee_mug_1	128	0	0
Coffee	coffee_mug_4	128	128	0
Coffee	coffee_mug_5	0	128	0
Coffee	coffee_mug_6	0	128	128
Flashlight	flashlight_1	64	0	0
Flashlight	flashlight_2	64	64	0
Flashlight	flashlight_3	0	64	0
Flashlight	flashlight_5	0	64	64
Soda	soda_can_1	0	0	192
Soda	soda_can_3	192	0	192
Soda	soda_can_4	64	64	64
Soda	soda_can_5	192	192	192
Soda	soda_can_6	128	128	128

Links

The code, data and paper can be downloaded from the following page.

https://hzhongresearch.github.io/

Licence

Copyright 2016 HENRY ZHONG. The data is released under CC BY 4.0. See LICENCE.txt.

If you use this work please cite our paper.

@inproceedings{zhong2016quickfind,
  title={Quickfind: Fast and contact-free object detection using a depth sensor},
  author={Zhong, Henry and Kanhere, Salil S and Chou, Chun Tung},
  booktitle={Pervasive Computing and Communication Workshops (PerCom Workshops), 2016 IEEE International Conference on},
  pages={1--6},
  year={2016},
  organization={IEEE}
}