GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
Paper

Requirements

• Use script to install packages.

Preparing datasets

GET3D is trained on synthetic dataset. Rendering scripts for Shapenet is provided. Please refer to readme to download shapenet dataset and render it.

Inference

Inference using Colab Environment

• Run inference.ipynb on colab

Inference using CLI

• Download pretrained model from here.
• Inference could operate on a single GPU with 16 GB memory.

python train_3d.py --outdir=save_inference_results/shapenet_car  --gpus=1 --batch=4 --gamma=40 --data_camera_mode shapenet_car  --dmtet_scale 1.0  --use_shapenet_split 1  --one_3d_generator 1  --fp32 0 --inference_vis 1 --resume_pretrain MODEL_PATH

Train the model

cd YOUR_CODE_PATH 
export PYTHONPATH=$PWD:$PYTHONPATH
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

• Train on the unified generator on cars.

python train_3d.py --outdir=PATH_TO_LOG --data=PATH_TO_RENDER_IMG --camera_path PATH_TO_RENDER_CAMERA --gpus=8 --batch=32 --gamma=40 --data_camera_mode shapenet_car  --dmtet_scale 1.0  --use_shapenet_split 1  --one_3d_generator 1  --fp32 0

If want to debug the model first, reduce the number of gpus to 1 and batch size to 4 via:

--gpus=1 --batch=4

Evaluation metrics

Compute FID

• To evaluate the model with FID metric, add one option to the inference command: --inference_compute_fid 1

Compute COV & MMD scores for LFD & CD

• First generate 3D objects for evaluation, add one option to the inference command: --inference_generate_geo 1
• Following README to compute metrics.