mvt's Introduction

MVT: Multi-view Vision Transformer for 3D Object Recognition

This folder contains the PyTorch code for our BMVC 2021 paper MVT: Multi-view Vision Transformer for 3D Object Recognition by Shuo Chen, Tan Yu, and Ping Li.

If you use this code for a paper, please cite:

@inproceedings{Chen2021MVT,
  author    = {Shuo Chen and
               Tan Yu and
               Ping Li},
  title     = {{MVT:} Multi-view Vision Transformer for 3D Object Recognition},
  booktitle = {{BMVC}},
  year      = {2021},
}

We have developed a MLP-based architecture for view-based 3D object recognition. Check out our paper R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition and the accompanying code repository for more information.

Requirements

PyTorch 1.7.0+

Data Preparation

Download the ModelNet40 dataset (20 view setting) and extract it to the current folder:

wget https://data.airc.aist.go.jp/kanezaki.asako/data/modelnet40v2png_ori4.tar
tar -xvf modelnet40v2png_ori4.tar

Training

Download the DeiT small model pretrained on ImageNet 2012 from the Model Zoo.

Train the model on 2 V100 GPUs:

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_addr 127.0.0.2 --master_port 23918 --nproc_per_node=2 --use_env main.py --model deit_small_patch16_224 --epochs 100 --batch-size 8 --lr 0.001 --dataset M10 --view-num 20 --output_dir outputs --num_workers 4

The training log is available here for your reference.

Note: If you change --view-num, please remember to change timm/models/vision_transformer.py line 316 accordingly:

x = x.reshape(B//20, N*20, C)

Evaluation

Run the following command for evaluation:

CUDA_VISIBLE_DEVICES=0 python main.py --eval --model=deit_tiny_patch16_224 --resume=trained/model/path.pth --data-set=M10 --num_workers=4 --view-num=20 --batch-size=8