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T4
| ## Scripts for Evaluating GET3D | |
| #### Compute Light Field Distance | |
| We thanks the authors for releasing the source code of | |
| LFD [official repo](https://github.com/Sunwinds/ShapeDescriptor) and | |
| It's [python extension](https://github.com/kacperkan/light-field-distance). | |
| - Step 0: Download the all the files | |
| from [official repo](https://github.com/Sunwinds/ShapeDescriptor/tree/master/LightField/3DRetrieval_v1.8/3DRetrieval_v1.8/Executable) | |
| , and save it into `evaluation_scripts/load_data`. | |
| - Step 1: Compile the files for light fild distance | |
| ```bash | |
| cd evaluation_scripts/load_data | |
| bash do_all.sh | |
| cd ../.. | |
| git clone https://github.com/kacperkan/light-field-distance | |
| cd light-field-distance | |
| bash compile.sh | |
| python setup.py install | |
| cd .. | |
| ``` | |
| - Step 2: To compute LFD on a server, we need to set up a dummy screen | |
| ```bash | |
| apt-get install -y freeglut3 libglu1-mesa xserver-xorg-video-dummy | |
| X -config evaluation_scripts/compute_lfd_feat/dummy-1920x1080.conf | |
| ``` | |
| - Step 3: On a separate console, `export DISPLAY=:0` | |
| - Step 4: We first generat the Light Field feature for each object by running | |
| ```bash | |
| python compute_lfd_feat_multiprocess.py --gen_path PATH_TO_THE_MODEL_PREDICTION --save_path PATH_FOR_LFD_OUTPUT_FOR_PRED | |
| ``` | |
| - Step 5: Do the same for the ground truth data | |
| ```bash | |
| python compute_lfd_feat_multiprocess.py --gen_path PATH_TO_GT_MODEL --save_path PATH_FOR_LFD_OUTPUT_FOR_GT | |
| ``` | |
| - Step 6: Compute the metric: LFD | |
| ```bash | |
| python compute_lfd.py --split_path PATH_TO_TEST_SPLIT --dataset_path PATH_FOR_LFD_OUTPUT_FOR_GT --gen_path PATH_FOR_LFD_OUTPUT_FOR_PRED --save_name results/our/lfd.pkl | |
| ``` | |
| ### Compute Chamfer Distance | |
| - Step 1: Download original shapenet obj files from Shapenet Webpage | |
| - Step 2: Running scripts to compute the chamfer distance | |
| ```bash | |
| python compute_cd.py --dataset_path PATH_TO_GT_OBJS --gen_path PATH_TO_THE_MODEL_PREDICTION --split_path PATH_TO_TEST_SPLIT --save_name results/our/cd.pkl | |
| ``` | |
| (Optional) For shapenet car, since the GT dataset contains intern structures, we thus only | |
| sample the points from the outer surface of the object for both our prediction and ground | |
| truth. To achieve this: | |
| ```bash | |
| python sample_surface.py --n_points 5000 --n_proc 2 --shape_root PATH_TO_OBJS --save_root PATH_TO_THE_SAMPLE_POINTS | |
| ``` | |
| ### Compute Cov and MMD score: | |
| After compute the chamfer distance and LFD, to compute the Coverage score and MMD score: | |
| ```bash | |
| python compute_cov_mmd.py | |
| ``` | |