Dataset Overview
The ZPark_2K Dataset contains images, depth maps and pixel level semantic labels of 2 kilometer urban street scenes.
The figure below shows one image set, including the orginal image, the overlayed segmented image, the depth map, and the color-coded semantic label map.
The raw data is aquired with a survey grade mobile scanner, together with high-resolution color cameras. The 3D point cloud has an average point density of 2cm and a depth accuracy of better than 1cm. The point cloud is precisely registered with color images. For each image, a synthesized depth map is rendered using the 3D point cloud. In this way, we created an outdoor high-precision RGB-D image sequence with per-pixel semantic labels.
In the following, we give an overview of design choices and detailed data specifications.
Features
- multiple scan: contains 10 scans of the same street scenes to provide illumination variation
- semi-auto labeling: 3002 samples with semi-auto pixel level labeling
Class Definitions
| class | class_id | category | category_id | color_code |
|---|---|---|---|---|
| others | 0 | others | 0 | 000000 |
| sky | 17 | sky | 1 | 87ceeb |
| motor vehicle | 33 | moving object | 2 | 400080 |
| non-motor vehicle | 34 | moving object | 2 | 4080c0 |
| pedestrian | 35 | moving object | 2 | 404000 |
| rider | 36 | moving object | 2 | 0080c0 |
| other flat | 48 | flat | 3 | 804080 |
| motorway | 49 | flat | 3 | 8000c0 |
| bicycle lane | 50 | flat | 3 | c00040 |
| sidewalk | 51 | flat | 3 | 8080c0 |
| other boundary | 64 | boundary | 4 | 804040 |
| curb | 65 | boundary | 4 | c080c0 |
| other roadblock | 80 | roadblock | 5 | c08040 |
| traffic cone | 81 | roadblock | 5 | 000040 |
| road pile | 82 | roadblock | 5 | 0000c0 |
| fence | 83 | roadblock | 5 | 404080 |
| other object | 96 | object | 6 | c04080 |
| street lamp | 97 | object | 6 | c08080 |
| traffic light | 98 | object | 6 | 004040 |
| pole | 99 | object | 6 | c0c080 |
| traffic sign | 100 | object | 6 | 4000c0 |
| billboard | 101 | object | 6 | c000c0 |
| bus stop board | 102 | object | 6 | c00080 |
| other construction | 112 | construction | 7 | 808000 |
| building | 113 | construction | 7 | 800000 |
| newsstand | 114 | construction | 7 | 408040 |
| security booth | 115 | construction | 7 | 808040 |
| other nature | 128 | nature | 8 | c0c000 |
| vegetation | 129 | nature | 8 | 40c000 |
* The current labeling process may lead to errorneous labels for fast moving objects. This is expected to be fixed around Septh 2017, when a much larger data set will also be released by then.
Format
RGB images
3002 8-bit images. The unzipped path is {record}/{camera}/{timestamp}_{camera}.jpg. Our acquisition system has mutiple cameras and split data to records automatically on time intervals.
file: Image.zip (4.1G, md5=3effa00dd59e9b72aac6b01dc6669c51)
Depth maps
16-bit depth maps for the corresponding images. The path structure is same with RGB images. Due to the limitation of the acquisition system, depth of moving object is inaccurate.
file: Depth.zip (1.7G, md5=9315cc3c8de83916b2454d3fe1485e78)
Labels
Pixel level semantic labels for the corresponding images. The label is stored as 8-bit color images using the color table defined in class definitions. The path structure is same with RGB images. Due to the limitaion the acquisition system, labels of moving object is inaccurate.
file: Label.zip (241M, md5=6a31bb41abe5b9a81a49903a9cecd46f)
Download
Please contact Dr. Ruigang Yang for details about accessing the data.
