Skip to main content

Neural mesh models for 3D reasoning.

Project description

https://img.shields.io/pypi/v/nemo.svg https://img.shields.io/travis/wufeim/nemo.svg Documentation Status

Neural mesh models for 3D reasoning.

Features

Models

  • Original NeMo (3D pose estimation)

  • 3D aware classification NeMo (3D pose estimation and classification)

  • 6D pose estimation NeMo

  • (Deformable NeMo ?)

Baselines

  • ResNet50

  • Starmap

  • Transformer

  • Faster RCNN

Datasets

  • OOD-CV

  • Pascal3D+

  • (Synthetic data ?)

  • (ObjectNet ?)

Requirements

Python Environment

  1. Create conda environment:

conda create -n nemo python=3.9
conda activate nemo
  1. Install PyTorch (see pytorch.org):

conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=10.2 -c pytorch
  1. Install PyTorch3D (see github.com/facebookresearch/pytorch3d):

conda install -c fvcore -c iopath -c conda-forge fvcore iopath
conda install -c bottler nvidiacub
conda install pytorch3d -c pytorch3d
  1. Install other dependencies:

conda install numpy matplotlib scipy scikit-image
conda install pillow
conda install -c conda-forge timm tqdm pyyaml transformers
pip install wget BboxTools opencv-python
  1. Install NeMo-Extensions:

pip install -e .

Data Preparation

See data/README.

TODO

  • [ ] Add support for VoGe Renderer

  • [ ] Add support for multiple GPUs support

  • [ ] Add support for some visualizations

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neural_mesh_model-1.0.0.tar.gz (40.4 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page