109 lines
3.3 KiB
Markdown
109 lines
3.3 KiB
Markdown
# Custom Dataset Tutorial
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For the custom dataset template, we only consider the basic scenario: raw point clouds and
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their corresponding annotations. Point clouds are supposed to be stored in `.npy` format.
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## Label format
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We only consider the most basic information -- category and bounding box in the label template.
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Annotations are stored in the `.txt`. Each line represents a box in a given scene as below:
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```
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# format: [x y z dx dy dz heading_angle category_name]
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1.50 1.46 0.10 5.12 1.85 4.13 1.56 Vehicle
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5.54 0.57 0.41 1.08 0.74 1.95 1.57 Pedestrian
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```
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The box should in the unified 3D box definition (see [README](../README.md))
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## Files structure
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Files should be placed as the following folder structure:
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```
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OpenPCDet
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├── data
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│ ├── custom
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│ │ │── ImageSets
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│ │ │ │── train.txt
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│ │ │ │── val.txt
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│ │ │── points
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│ │ │ │── 000000.npy
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│ │ │ │── 999999.npy
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│ │ │── labels
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│ │ │ │── 000000.txt
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│ │ │ │── 999999.txt
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├── pcdet
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├── tools
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```
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Dataset splits need to be pre-defined and placed in `ImageSets`
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## Hyper-parameters Configurations
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### Point cloud features
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Modify following configurations in `custom_dataset.yaml` to
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suit your own point clouds.
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```yaml
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POINT_FEATURE_ENCODING: {
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encoding_type: absolute_coordinates_encoding,
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used_feature_list: ['x', 'y', 'z', 'intensity'],
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src_feature_list: ['x', 'y', 'z', 'intensity'],
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}
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...
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# In gt_sampling data augmentation
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NUM_POINT_FEATURES: 4
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```
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#### Point cloud range and voxel sizes
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For voxel based detectors such as SECOND, PV-RCNN and CenterPoint, the point cloud range and voxel size should follow:
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1. Point cloud range along z-axis / voxel_size is 40
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2. Point cloud range along x&y-axis / voxel_size is the multiple of 16.
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Notice that the second rule also suit pillar based detectors such as PointPillar and CenterPoint-Pillar.
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### Category names and anchor sizes
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Category names and anchor size are need to be adapted to custom datasets.
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```yaml
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CLASS_NAMES: ['Vehicle', 'Pedestrian', 'Cyclist']
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...
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MAP_CLASS_TO_KITTI: {
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'Vehicle': 'Car',
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'Pedestrian': 'Pedestrian',
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'Cyclist': 'Cyclist',
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}
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...
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'anchor_sizes': [[3.9, 1.6, 1.56]],
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...
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# In gt sampling data augmentation
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PREPARE: {
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filter_by_min_points: ['Vehicle:5', 'Pedestrian:5', 'Cyclist:5'],
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filter_by_difficulty: [-1],
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}
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SAMPLE_GROUPS: ['Vehicle:20','Pedestrian:15', 'Cyclist:15']
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...
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```
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In addition, please also modify the default category names for creating infos in `custom_dataset.py`
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```
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create_custom_infos(
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dataset_cfg=dataset_cfg,
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class_names=['Vehicle', 'Pedestrian', 'Cyclist'],
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data_path=ROOT_DIR / 'data' / 'custom',
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save_path=ROOT_DIR / 'data' / 'custom',
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)
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```
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## Create data info
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Generate the data infos by running the following command:
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```shell
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python -m pcdet.datasets.custom.custom_dataset create_custom_infos tools/cfgs/dataset_configs/custom_dataset.yaml
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```
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## Evaluation
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Here, we only provide an implementation for KITTI stype evaluation.
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The category mapping between custom dataset and KITTI need to be defined
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in the `custom_dataset.yaml`
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```yaml
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MAP_CLASS_TO_KITTI: {
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'Vehicle': 'Car',
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'Pedestrian': 'Pedestrian',
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'Cyclist': 'Cyclist',
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}
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```
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