facebookresearch/meshrcnn
code for Mesh R-CNN, an academic publication, presented at ICCV 2019
| repo name | facebookresearch/meshrcnn |
| repo link | https://github.com/facebookresearch/meshrcnn |
| homepage | |
| language | Python |
| size (curr.) | 111 kB |
| stars (curr.) | 527 |
| created | 2020-01-07 |
| license | Other |
Mesh R-CNN
Code for the paper
Mesh R-CNN
Georgia Gkioxari, Jitendra Malik, Justin Johnson
ICCV 2019
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.
License
The Mesh R-CNN codebase is released under BSD-3-Clause License
