| sort |
|---|
2 |
The labelled pointclouds are released as pcd files. To read an pcd file into a numpy array, we recommend the package pypcd.
<style type="text/css"> .tg {border-collapse:collapse;border-spacing:0;} .tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px; overflow:hidden;padding:10px 5px;word-break:normal;} .tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px; font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;} .tg .tg-lboi{border-color:inherit;text-align:left;vertical-align:middle} .tg .tg-9wq8{border-color:inherit;text-align:center;vertical-align:middle} .tg .tg-a890{background-color:#FFF;border-color:inherit;color:#212529;text-align:left;vertical-align:middle} .tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:top} .tg .tg-uzvj{border-color:inherit;font-weight:bold;text-align:center;vertical-align:middle} .tg .tg-nzoj{border-color:inherit;color:#00E;text-align:left;text-decoration:underline;vertical-align:middle} .tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top} </style>| Sequence | # scans | Preview |
|---|---|---|
| ntu_day_01 | 6010 |  |
| ntu_day_02 | 2273 |  |
| ntu_day_10 | 3232 |  |
| ntu_night_13 | 2323 |  |
| kth_day_06 | 8893 |  |
| kth_day_09 | 7655 |  |
| kth_night_05 | 6638 |  |
| tuhh_day_02 | 4986 |  |
| tuhh_day_03 | 8376 |  |
| tuhh_night_08 | 7075 |  |
| tuhh_night_09 | 1832 |  |
The classes corresponding to the label value are as follows:
{0: 'barrier',
1: 'bike',
2: 'building',
3: 'chair',
4: 'cliff',
5: 'container',
6: 'curb',
7: 'fence',
8: 'hydrant',
9: 'infosign',
10: 'lanemarking',
11: 'noise',
12: 'other',
13: 'parkinglot',
14: 'pedestrian',
15: 'pole',
16: 'road',
17: 'shelter',
18: 'sidewalk',
19: 'stairs',
20: 'structure-other',
21: 'traffic-cone',
22: 'traffic-sign',
23: 'trashbin',
24: 'treetrunk',
25: 'vegetation',
26: 'vehicle-dynamic',
27: 'vehicle-other',
28: 'vehicle-static'}