Currently we provide the dataloader of KITTI, Waymo.
- Please follow the instructions from OpenPCDet. We adopt the same data generation process.
detection
├── data
│ ├── kitti
│ │ │── ImageSets
│ │ │── training
│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
│ │ │── testing
│ │ │ ├──calib & velodyne & image_2
├── al3d_det
├── tools
- Generate the data infos by running the following command:
python -m al3d_det.datasets.kitti_dataset.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
- Please download the official Waymo Open Dataset,
including the training data
training_0000.tar~training_0031.tar
and the validation datavalidation_0000.tar~validation_0007.tar
. - Unzip all the above
xxxx.tar
files to the directory ofdata/waymo/raw_data
as follows (You could get 798 train tfrecord and 202 val tfrecord ):
Currently we provide the dataloader of Waymo dataset, and the supporting of more datasets are on the way.
-
please place the data folder based on the following structure:
detection ├── data │ ├── waymo │ │ │── ImageSets │ │ │── raw_data │ │ │ │── segment-xxxxxxxx.tfrecord │ │ │ │── .... ├── al3d_det ├── tools
-
process waymo infos:
cd detection python -m al3d_det.datasets.waymo.waymo_preprocess --cfg_file tools/cfgs/det_dataset_cfgs/waymo_xxx_sweeps_mm.yaml --func create_waymo_infos
-
generate database for gt-sampling
cd detection python -m al3d_det.datasets.waymo.waymo_preprocess --cfg_file tools/cfgs/det_dataset_cfgs/waymo_xxxx_sweeps_mm.yaml --func create_waymo_database
- Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours,
and you could refer to
data/waymo/waymo_processed_data
to see how many records that have been processed):
Note that you do not need to install waymo-open-dataset
if you have already processed the data before and do not need to evaluate with official Waymo Metrics.