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

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.

Over9000 optimizer

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

High-fidelity performance metrics for generative models in PyTorch

OptNet: Differentiable Optimization as a Layer in Neural Networks

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

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

A tiny package to compare two neural networks in PyTorch

PyGCL: Graph Contrastive Learning Library for PyTorch

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

PyTorch Lightning Optical Flow models, scripts, and pretrained weights.

A Pytorch Implementation for Compact Bilinear Pooling.

ocaml-torch provides some ocaml bindings for the PyTorch tensor library.

TorchSSL: A PyTorch-based Toolbox for Semi-Supervised Learning

A lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.

S3-plugin is a high performance PyTorch dataset library to efficiently access datasets stored in S3 buckets.

Fast, general, and tested differentiable structured prediction in PyTorch

Kaldi-compatible feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd

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

Bunch of optimizer implementations in PyTorch

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

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.

Over9000 optimizer

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

High-fidelity performance metrics for generative models in PyTorch

OptNet: Differentiable Optimization as a Layer in Neural Networks

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

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

A tiny package to compare two neural networks in PyTorch

PyGCL: Graph Contrastive Learning Library for PyTorch

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

PyTorch Lightning Optical Flow models, scripts, and pretrained weights.

A Pytorch Implementation for Compact Bilinear Pooling.

ocaml-torch provides some ocaml bindings for the PyTorch tensor library.

TorchSSL: A PyTorch-based Toolbox for Semi-Supervised Learning

A lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.

S3-plugin is a high performance PyTorch dataset library to efficiently access datasets stored in S3 buckets.

Fast, general, and tested differentiable structured prediction in PyTorch

Kaldi-compatible feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd

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

Bunch of optimizer implementations in PyTorch

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