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

Last update: Dec 21, 2022

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

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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

TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards

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

This is an differentiable pytorch implementation of SIFT patch descriptor.

PyTorch implementations of normalizing flow and its variants.

Fast Discounted Cumulative Sums in PyTorch

Use Jax functions in Pytorch with DLPack

On the Variance of the Adaptive Learning Rate and Beyond

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

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

Over9000 optimizer

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

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

A PyTorch implementation of L-BFGS.

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

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

A Closer Look at Structured Pruning for Neural Network Compression

Reformer, the efficient Transformer, in Pytorch

Implements pytorch code for the Accelerated SGD algorithm.

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

Tutorial for surrogate gradient learning in spiking neural networks

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