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

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]

image

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 \
Owner
Zai Shi
Computer Science, ETH Zürich
Zai Shi
torch-optimizer -- collection of optimizers for Pytorch

torch-optimizer torch-optimizer -- collection of optimizers for PyTorch compatible with optim module. Simple example import torch_optimizer as optim

Nikolay Novik 2.6k Jan 03, 2023
PyGCL: Graph Contrastive Learning Library for PyTorch

PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL components from published papers, standardized evaluation, and experiment management.

GCL: Graph Contrastive Learning Library for PyTorch 592 Jan 07, 2023
A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

Code release for "Bayesian Compression for Deep Learning" In "Bayesian Compression for Deep Learning" we adopt a Bayesian view for the compression of

Karen Ullrich 190 Dec 30, 2022
A code copied from google-research which named motion-imitation was rewrited with PyTorch

motor-system Introduction A code copied from google-research which named motion-imitation was rewrited with PyTorch. More details can get from this pr

NewEra 6 Jan 08, 2022
Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS

(Generic) EfficientNets for PyTorch A 'generic' implementation of EfficientNet, MixNet, MobileNetV3, etc. that covers most of the compute/parameter ef

Ross Wightman 1.5k Jan 01, 2023
On the Variance of the Adaptive Learning Rate and Beyond

RAdam On the Variance of the Adaptive Learning Rate and Beyond We are in an early-release beta. Expect some adventures and rough edges. Table of Conte

Liyuan Liu 2.5k Dec 27, 2022
Reformer, the efficient Transformer, in Pytorch

Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH

Phil Wang 1.8k Jan 06, 2023
A very simple and small path tracer written in pytorch meant to be run on the GPU

MentisOculi Pytorch Path Tracer A very simple and small path tracer written in pytorch meant to be run on the GPU Why use pytorch and not some other c

Matthew B. Mirman 222 Dec 01, 2022
Bunch of optimizer implementations in PyTorch

Bunch of optimizer implementations in PyTorch

Hyeongchan Kim 76 Jan 03, 2023
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

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

Cong Cai 12 Dec 19, 2021
High-fidelity performance metrics for generative models in PyTorch

High-fidelity performance metrics for generative models in PyTorch

Vikram Voleti 5 Oct 24, 2021
Kaldi-compatible feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd

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

Fangjun Kuang 119 Jan 03, 2023
A lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.

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

Fidelity Investments 56 Sep 13, 2022
A simplified framework and utilities for PyTorch

Here is Poutyne. Poutyne is a simplified framework for PyTorch and handles much of the boilerplating code needed to train neural networks. Use Poutyne

GRAAL/GRAIL 534 Dec 17, 2022
TorchSSL: A PyTorch-based Toolbox for Semi-Supervised Learning

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

1k Dec 28, 2022
High-level batteries-included neural network training library for Pytorch

Pywick High-Level Training framework for Pytorch Pywick is a high-level Pytorch training framework that aims to get you up and running quickly with st

382 Dec 06, 2022
Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

News SRU++, a new SRU variant, is released. [tech report] [blog] The experimental code and SRU++ implementation are available on the dev branch which

ASAPP Research 2.1k Jan 01, 2023
A PyTorch implementation of L-BFGS.

PyTorch-LBFGS: A PyTorch Implementation of L-BFGS Authors: Hao-Jun Michael Shi (Northwestern University) and Dheevatsa Mudigere (Facebook) What is it?

Hao-Jun Michael Shi 478 Dec 27, 2022
PyTorch toolkit for biomedical imaging

farabio is a minimal PyTorch toolkit for out-of-the-box deep learning support in biomedical imaging. For further information, see Wikis and Docs.

San Askaruly 47 Dec 28, 2022
Implements pytorch code for the Accelerated SGD algorithm.

AccSGD This is the code associated with Accelerated SGD algorithm used in the paper On the insufficiency of existing momentum schemes for Stochastic O

205 Jan 02, 2023