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KITTI.md

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KITTI

Download

Download from continuous_view_synthesis. Store the files in ${KITTI_HOME}/dataset_kitti.

Train

Update options

Update the paths in ./options/options.py for the dataset being used.

Training scripts

Use the ./train.sh to train one of the models on a single GPU node.

You can also look at ./submit_slurm_synsin.sh to see how to modify parameters in the renderer and run on a slurm cluster.

Evaluate

To evaluate, we run the following script. This gives us a bunch of generated vs ground truth images.

export KITTI=${KITTI_HOME}/dataset_kitti/images
python evaluation/eval_kitti.py --old_model ${OLD_MODEL} --result_folder ${TEST_FOLDER}

We then compare the generated to ground truth images using the evaluation script.

python evaluation/evaluate_perceptualsim.py \
     --folder ${TEST_FOLDER} \
     --pred_image im_B.png \
     --target_image im_res.png \
     --output_file kitti_results

The results we get for each model is given in RESULTS.md.

If you do not get approximately the same results (some models use noise as input, so there is some randomness), then there is probably an error in your setup:

  • Check the libraries.
  • Check the data setup is indeed correct.