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

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

Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking"

A very simple and small path tracer written in pytorch meant to be run on the GPU

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

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

A code copied from google-research which named motion-imitation was rewrited with PyTorch

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

lookahead optimizer (Lookahead Optimizer: k steps forward, 1 step back) for pytorch

An optimizer that trains as fast as Adam and as good as SGD.

OptNet: Differentiable Optimization as a Layer in Neural Networks

Pytorch implementation of Distributed Proximal Policy Optimization

A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

PyTorch toolkit for biomedical imaging

Fast, general, and tested differentiable structured prediction in PyTorch

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

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

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

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

ONNX Runtime for PyTorch accelerates PyTorch model training using ONNX Runtime.

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

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

Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking"

A very simple and small path tracer written in pytorch meant to be run on the GPU

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

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

A code copied from google-research which named motion-imitation was rewrited with PyTorch

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

lookahead optimizer (Lookahead Optimizer: k steps forward, 1 step back) for pytorch

An optimizer that trains as fast as Adam and as good as SGD.

OptNet: Differentiable Optimization as a Layer in Neural Networks

Pytorch implementation of Distributed Proximal Policy Optimization

A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

PyTorch toolkit for biomedical imaging

Fast, general, and tested differentiable structured prediction in PyTorch

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

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

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

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

ONNX Runtime for PyTorch accelerates PyTorch model training using ONNX Runtime.

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