Here we introduce some tools to help you:

• visualize the data and results.
• convert the pretrained model from others.
• ... More to come.

run tools/visualization.py to view the scene flow dataset with ground truth flow. Note the color wheel in under world coordinate.

# Visualize flow with color coding
python tools/visualization.py vis --data_dir /path/to/data --res_name flow

# Compare multiple results side-by-side
python tools/visualization.py vis --data_dir /path/to/data --res_name "[flow, deflow, deltaflow, ssf]"

# Show flow as vector lines
python tools/visualization.py vector --data_dir /path/to/data

# Check flow with pc0, pc1, and flowed pc0
python tools/visualization.py check --data_dir /path/to/data

# Show error heatmap
python tools/visualization.py error --data_dir /path/to/data --res_name "[flow, deflow, deltaflow, ssf]"

Demo Effect (press SPACE to stop and start in the visualization window):

Tips: To quickly create qualitative results for all methods, you can use multiple results comparison mode, select a good viewpoint and then save screenshots for all frames by pressing P key. You will found all methods' results are saved in the output folder (default is logs/imgs).

You can quickly read all keys and shapes in a .h5 file by:

python tools/read_h5.py --file_path /path/to/file.h5

run tools/zero2ours.py to convert the ZeroFlow pretrained model to our codebase.

python tools/zero2ours.py --model_path /home/kin/nsfp_distilatation_3x_49_epochs.ckpt --reference_path /home/kin/fastflow3d.ckpt --output_path /home/kin/zeroflow3x.ckpt

• model_path, you can download from: kylevedder/zeroflow_weights
• reference_path, you can download fastflow3d model from: zendo
• output_path, the converted model path. You can then run any evaluation script and visualization script with the converted model.