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

Fast Discounted Cumulative Sums in PyTorch

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

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torch-optimizer -- collection of optimizers for Pytorch

PyTorch to TensorFlow Lite converter

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

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High-fidelity performance metrics for generative models in PyTorch

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

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

Fast Discounted Cumulative Sums in PyTorch

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

PyNIF3D is an open-source PyTorch-based library for research on neural implicit functions (NIF)-based 3D geometry representation.

Use Jax functions in Pytorch with DLPack

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

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

A PyTorch implementation of L-BFGS.

Unofficial PyTorch implementation of DeepMind's Perceiver IO with PyTorch Lightning scripts for distributed training

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

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

PyTorch wrappers for using your model in audacity!

PyTorch Extension Library of Optimized Scatter Operations

Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking"

torch-optimizer -- collection of optimizers for Pytorch

PyTorch to TensorFlow Lite converter

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

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.

High-fidelity performance metrics for generative models in PyTorch

Pytorch implementation of Distributed Proximal Policy Optimization

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

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