Supervisely Point Cloud#

Format specification#

Specification for the Point Cloud data format is available here.

You can also find examples of working with the dataset here.

Supported annotation types:

cuboid_3d

Supported annotation attributes:

track_id (read/write, integer), responsible for object field
createdAt (write, string),
updatedAt (write, string),
labelerLogin (write, string), responsible for the corresponding fields in the annotation file.
arbitrary attributes

Supported image attributes:

description (read/write, string),
createdAt (write, string),
updatedAt (write, string),
labelerLogin (write, string), responsible for the corresponding fields in the annotation file.
frame (read/write, integer). Indicates frame number of the image.
arbitrary attributes

Import Supervisely Point Cloud dataset#

An example dataset in Supervisely Point Cloud format is available for download:

https://drive.google.com/u/0/uc?id=1BtZyffWtWNR-mk_PHNPMnGgSlAkkQpBl&export=download

Point Cloud dataset directory should have the following structure:

└─ Dataset/
    ├── ds0/
    │   ├── ann/
    │   │   ├── <pcdname1.pcd.json>
    │   │   ├── <pcdname2.pcd.json>
    │   │   └── ...
    │   ├── pointcloud/
    │   │   ├── <pcdname1.pcd>
    │   │   ├── <pcdname1.pcd>
    │   │   └── ...
    │   ├── related_images/
    │   │   ├── <pcdname1_pcd>/
    │   │   |  ├── <image_name.ext.json>
    │   │   |  ├── <image_name.ext.json>
    │   │   └── ...
    ├── key_id_map.json
    └── meta.json

Use the command below to import a Supervisely Point Cloud dataset:

datum project create
datum project import -f sly_pointcloud <path/to/dataset>

To make sure that the selected dataset has been added to the project, you can run datum project info, which will display the project and dataset information.

Export to other formats#

Datumaro can convert Supervisely Point Cloud dataset into any other format Datumaro supports.

Such conversion will only be successful if the output format can represent the type of dataset you want to convert, e.g. 3D point clouds can be saved in KITTI Raw format, but not in COCO keypoints.

There are several ways to convert a Supervisely Point Cloud dataset to other dataset formats:

datum project create
datum project import -f sly_pointcloud <path/to/sly_pcd/>
datum project export -f kitti_raw -o <output/dir>

or

datum convert -if sly_pointcloud -i <path/to/sly_pcd/> -f kitti_raw

Or, using Python API:

import datumaro as dm

dataset = dm.Dataset.import_from('<path/to/dataset>', 'sly_pointcloud')
dataset.export('save_dir', 'kitti_raw', save_media=True)

Export to Supervisely Point Cloud#

There are several ways to convert a dataset to Supervisely Point Cloud format:

# export dataset into Supervisely Point Cloud format from existing project
datum project export -p <path/to/project> -f sly_pointcloud -o <output/dir> \
    -- --save-media

# converting to Supervisely Point Cloud format from other format
datum convert -if kitti_raw -i <path/to/dataset> \
    -f sly_pointcloud -o <output/dir> -- --save-media

Extra options for exporting in Supervisely Point Cloud format:

--save-media allow to export dataset with saving media files. This will include point clouds and related images (by default False)
--image-ext IMAGE_EXT allow to specify image extension for exporting dataset (by default - keep original or use .png, if none)
--reindex assigns new indices to frames and annotations.
--allow-undeclared-attrs allows writing arbitrary annotation attributes. By default, only attributes specified in the input dataset metainfo will be written.

Examples#

Example 1. Import dataset, compute statistics#

datum project create -o project
datum project import -p project -f sly_pointcloud ../sly_dataset/
datum stats -p project

Example 2. Convert Supervisely Point Clouds to KITTI Raw#

datum convert -if sly_pointcloud -i ../sly_pcd/ \
    -f kitti_raw -o my_kitti/ -- --save-media --reindex --allow-attrs

Example 3. Create a custom dataset#

import datumaro as dm

dataset = dm.Dataset.from_iterable([
    dm.DatasetItem(id='frame_1',
        annotations=[
            dm.Cuboid3d(id=206, label=0,
                position=[320.86, 979.18, 1.04],
                attributes={'occluded': False, 'track_id': 1, 'x': 1}),

            dm.Cuboid3d(id=207, label=1,
                position=[318.19, 974.65, 1.29],
                attributes={'occluded': True, 'track_id': 2}),
        ],
        pcd='path/to/pcd1.pcd',
        attributes={'frame': 0, 'description': 'zzz'}
    ),

    dm.DatasetItem(id='frm2',
        annotations=[
            dm.Cuboid3d(id=208, label=1,
                position=[23.04, 8.75, -0.78],
                attributes={'occluded': False, 'track_id': 2})
        ],
        pcd='path/to/pcd2.pcd', related_images=['image2.png'],
        attributes={'frame': 1}
    ),
], categories=['cat', 'dog'])

dataset.export('my_dataset/', format='sly_pointcloud', save_media=True,
    allow_undeclared_attrs=True)

Examples of using this format from the code can be found in the format tests