/ Machine Learning

Code for Mesh R-CNN an academic publication

Code for Mesh R-CNN an academic publication

Mesh R-CNN

code for Mesh R-CNN, an academic publication, presented at ICCV 2019.

Mesh-R-CNN

Installation Requirements

The implementation of Mesh R-CNN is based on Detectron2 and PyTorch3D.
You will first need to install those in order to be able to run Mesh R-CNN.

To install

git clone https://github.com/facebookresearch/meshrcnn.git
cd meshrcnn && pip install -e .

Demo

Run Mesh R-CNN on an input image

python demo/demo.py \
--config-file configs/pix3d/meshrcnn_R50_FPN.yaml \
--input /path/to/image \
--output output_demo \
--onlyhighest MODEL.WEIGHTS meshrcnn://meshrcnn_R50.pth

See demo.py for more details.

Running Experiments

Pix3D

See INSTRUCTIONS_PIX3D.md for more instructions.

ShapeNet

See INSTRUCTIONS_SHAPENET.md for more instructions.

GitHub