3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers (BMVC 2021)
Zai Shi*, Zhao Meng*, Yiran Xing, Yunpu Ma, Roger Wattenhofer
∗The first two authors contribute equally to this work
[BMVC (with presentation)] [Paper] [Supplementary]
Citation
@inproceedings{3d-retr,
author = {Zai Shi, Zhao Meng, Yiran Xing, Yunpu Ma, Roger Wattenhofer},
title = {3D-RETR: End-to-End Single and Multi-View3D Reconstruction with Transformers},
booktitle = {BMVC},
year = {2021}
}
Create Environment
git clone [email protected]:FomalhautB/3D-RETR.git
cd 3D-RETR
conda env create -f config/environment.yaml
conda activate 3d-retr
Prepare Data
ShapeNet
Download the Rendered Images and Voxelization (32) and decompress them into $SHAPENET_IMAGE
and $SHAPENET_VOXEL
Train
Here is an example of reproducing the result of the single view 3D-RETR-B on the ShapeNet dataset:
python train.py \
--model image2voxel \
--transformer_config config/3d-retr-b.yaml \
--annot_path data/ShapeNet.json \
--model_path $SHAPENET_VOX \
--image_path $SHAPENET_IMAGES \
--gpus 1 \
--precision 16 \
--deterministic \
--train_batch_size 16 \
--val_batch_size 16 \
--num_workers 4 \
--check_val_every_n_epoch 1 \
--accumulate_grad_batches 1 \
--view_num 1 \
--sample_batch_num 0 \
--loss_type dice \