Data preparation

Tri3D is capable of loading most datasets without preprocessing, except for the ones mentioned on this page.

NuScenes

The nuScenes dataset is missing some of the ego pose information, namely the position in z and the rotations along x and y. This can cause issues for temporal aggregation methods where aggregated point clouds and annotation boxes are thus not superimposed.

To remedy this, a SLAM based algorithm is provided which will optimize the alignement between pairs of point clouds and regress the missing values.

You can download pre-computed poses which we distribute as JSON patch files. The following commands will download and create new data splits (v1.1-mini and v1.1-trainval) with corrected poses:

pip install jsonpatch

curl -L -O https://github.com/CEA-LIST/tri3d/releases/download/v0.2.0/v1.1-mini.tar.gz
cp -r v1.0-mini v1.1-mini
tar xf v1.1-mini.tar.gz
jsonpatch v1.0-mini/ego_pose.json v1.1-mini/ego_pose.jsonpatch > v1.1-mini/ego_pose.json
jsonpatch v1.0-mini/sample_annotation.json v1.1-mini/sample_annotation.jsonpatch > v1.1-mini/sample_annotation.json

curl -L -O https://github.com/CEA-LIST/tri3d/releases/download/v0.2.0/v1.1-trainval.tar.gz
cp -r v1.0-trainval v1.1-trainval
tar xf v1.1-trainval.tar.gz
jsonpatch v1.0-trainval/ego_pose.json v1.1-trainval/ego_pose.jsonpatch > v1.1-trainval/ego_pose.json
jsonpatch v1.0-trainval/sample_annotation.json v1.1-trainval/sample_annotation.jsonpatch > v1.1-trainval/sample_annotation.json

In order to recompute the aligned poses, install the align_nuscenes extra dependencies and invoke the script like so:

python -m tri3d.datasets.align_nuscenes \
    --root ~/Datasets/NuScenes \
    --subset v1.0-mini \
    --out ~/Datasets/NuScenes/v1.1-mini

Once

Each split (“train”, “val”, “test”, “raw”) should be a different subfolder inside the root dataset directory. The file hierarchy of each split should follow the original organization.

Assuming all archive are stored together, the following commands will decompress the whole dataset as required:

find . -name 'train_*.tar' -exec tar --transform="s,^,train/," -xf {} \;
find . -name 'val_*.tar' -exec tar --transform="s,^,val/," -xf {} \;
find . -name 'test_*.tar' -exec tar --transform="s,^,test/," -xf {} \;
find . -name 'raw_*.tar' -exec tar --transform="s,^,raw/," -xf {} \;
find . -name 'raw_*.tar' -exec tar --transform="s,^,raw/," -xf {} \;
cat raw_lidar_p*.tar.parta* | tar --transform="s,^,raw/," -xf

Waymo

Tri3d supports the Waymo dataset with parquet file format. However, its files must be re-encoded with better chunking and sorting parameters to allow faster data loading.

To optimize the sequences in a folder, use the following command:

python -m tri3d.datasets.optimize_waymo \
    --input waymo_open_dataset_v_2_0_1 \
    --output optimized_waymo \
    --workers 4

The resulting files in the output directory will contain the same data but sorted, chunked and compressed with better settings.

Warning

The script uses a lot of memory and may cause OOM.