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

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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

PyTorch Extension Library of Optimized Scatter Operations

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

A PyTorch implementation of Learning to learn by gradient descent by gradient descent

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

Pytorch implementation of Distributed Proximal Policy Optimization

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

pip install antialiased-cnns to improve stability and accuracy

Official implementations of EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis.

Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS

Tacotron 2 - PyTorch implementation with faster-than-realtime inference

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

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

Fast, general, and tested differentiable structured prediction in PyTorch

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

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

An implementation of Performer, a linear attention-based transformer, in Pytorch

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

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

A tiny package to compare two neural networks in PyTorch

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.

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