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

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

The goal of this library is to generate more helpful exception messages for numpy/pytorch matrix algebra expressions.

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

Pytorch implementation of Distributed Proximal Policy Optimization

This is an differentiable pytorch implementation of SIFT patch descriptor.

PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

Fast Discounted Cumulative Sums in PyTorch

pip install antialiased-cnns to improve stability and accuracy

PyTorch extensions for fast R&D prototyping and Kaggle farming

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

PyGCL: Graph Contrastive Learning Library for PyTorch

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

PyTorch to TensorFlow Lite converter

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

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

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

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

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