3D-RETR: End-to-End Single and Multi-View3D Reconstruction with Transformers

Last update: Dec 21, 2022

Related tags

Pytorch Utilities 3D-RETR

Overview

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 \

3D-RETR: End-to-End Single and Multi-View3D Reconstruction with Transformers

Related tags

Overview

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

Citation

Create Environment

Prepare Data

ShapeNet

Train

Owner

Zai Shi

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

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3D-RETR: End-to-End Single and Multi-View3D Reconstruction with Transformers

Related tags

Overview

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

Citation

Create Environment

Prepare Data

ShapeNet

Train

Owner

Zai Shi

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

Tez is a super-simple and lightweight Trainer for PyTorch. It also comes with many utils that you can use to tackle over 90% of deep learning projects in PyTorch.

Fast Discounted Cumulative Sums in PyTorch

OptNet: Differentiable Optimization as a Layer in Neural Networks

High-level batteries-included neural network training library for Pytorch

PyTorch to TensorFlow Lite converter

Pytorch bindings for Fortran

A Closer Look at Structured Pruning for Neural Network Compression

PyTorch Extension Library of Optimized Scatter Operations

A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

Model summary in PyTorch similar to `model.summary()` in Keras

PyTorch framework A simple and complete framework for PyTorch, providing a variety of data loading and simple task solutions that are easy to extend and migrate

pip install antialiased-cnns to improve stability and accuracy

A tiny package to compare two neural networks in PyTorch

Use Jax functions in Pytorch with DLPack

Distiller is an open-source Python package for neural network compression research.

PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

PyTorch wrappers for using your model in audacity!

Learning Sparse Neural Networks through L0 regularization

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

Zai Shi, Zhao Meng, Yiran Xing, Yunpu Ma, Roger Wattenhofer