A Deep Learning Approach To Object Affordance Segmentation

This repo contains code and data samples from our encoder-decoder model, discussed in "A Deep Learning Approach To Object Affordance Segmentation" ICASSP, 2020, and extended in a journal that is under review in IEEE Access.

Prerequisites

The following are the minimum requirements to replicate the paper experiments:

• Python 3.7.2
• PyTorch 1.0.1
• CUDA 9.1
• Visdom (follow the steps here)

SOR3D-AFF samples

RGB - original resolution (1920x1080)

RGB - aligned with depth maps (512x424)

3D optical flow - after preprocessing (300x300)

Segmentation masks - Last frame only (512x424)

Train

python train.py --train_path path/to/dataset

Citation

If you use any code or model from this repo, please cite the following:

@inproceedings{thermos2020affordance,
  author       = "Spyridon Thermos and Petros Daras and Gerasimos Potamianos",
  title        = "A Deep Learning Approach To Object Affordance Segmentation",
  booktitle    = "Proc. International Conference on Acoustics Speech and Signal Processing (ICASSP)",
  year         = "2020"
}

License

Our code is released under MIT License (see LICENSE file for details)