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## 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 | |
``` | |