CO3D: Common Objects In 3D

This repository contains a set of tools for working with the Common Objects in 3D (CO3D) dataset.

Download the dataset

The dataset can be downloaded from the following Facebook AI Research web page: download link

Installation

This is a Python 3 / PyTorch codebase.

1. Install PyTorch.
2. Install PyTorch3D.
3. Install the remaining dependencies in requirements.txt:

pip install lpips visdom tqdm

Note that the core data model in dataset/types.py is independent of PyTorch and can be imported and used with other machine-learning frameworks.

Dependencies

• PyTorch
• PyTorch3D
• tqdm
• visdom
• lpips

Getting started

1. Install dependencies - See Installation above.
2. Download the dataset here to a given root folder DATASET_ROOT_FOLDER.
3. In dataset/dataset_zoo.py set the DATASET_ROOT variable to your DATASET_ROOT_FOLDER`:

   dataset_zoo.py:25: DATASET_ROOT = DATASET_ROOT_FOLDER
   

4. Run eval_demo.py:

   python eval_demo.py
   

   Note that eval_demo.py runs an evaluation of a simple depth-based image rendering (DBIR) model on the same data as in the paper. Hence, the results are directly comparable to the numbers reported in the paper.

Running tests

Unit tests can be executed with:

python -m unittest

License

The CO3D codebase is released under the BSD License.

Overview video

The following presentation of the dataset was delivered at the Extreme Vision Workshop at CVPR 2021: