quanw8781/orcnn

Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation

This is my own re-implementation model from ORCNN and only used for baseline model in our researching. The model is built based on the Dectectron2 and there is no official code that was used to obtain the results of the paper. Please reached the contact below if there is any concerns about the source code:

Email: [email protected]

ORCNN in Detectron2

Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation
Waiyu Lam Instructor: Yong Jae Lee

Occlusion-aware RCNN propose an all-in-one, end to end trainable multi-task model for semantic segmentation that simultaneously predicts amodal masks, visible masks, and occlusion masks for each object instance in an image in a single forward pass. On the COCO amodal dataset, ORCNN outperforms the current baseline for amodal segmentation by a large margin.

The amodal mask is defined as the union of the visible mask and the invisible occlusion mask of the object.
Person:

Bench:

In this repository, we provide the code to train and evaluate ORCNN. We also provide tools to visualize occlusion mask annotation and results.

Installation

See INSTALL.md.

Quick Start

Inference with Pre-trained Models

See Getting Started Amodal

Training & Evaluation & Visualization

See Getting Started ORCNN

License

Detectron2 is released under the Apache 2.0 license.

Citing ORCNN

@inproceedings{follmann2019learning,

  author    = {Patrick Follmann and

               Rebecca K{\"{o}}nig and

               Philipp H{\"{a}}rtinger and

               Michael Klostermann and

               Tobias B{\"{o}}ttger},

  title     = {Learning to See the Invisible: End-to-End Trainable Amodal Instance

               Segmentation},

  booktitle = {{IEEE} Winter Conference on Applications of Computer Vision, {WACV}

               2019, Waikoloa Village, HI, USA, January 7-11, 2019},

  pages     = {1328--1336},

  publisher = {{IEEE}},

  year      = {2019},

  url       = {https://doi.org/10.1109/WACV.2019.00146},

  doi       = {10.1109/WACV.2019.00146},
}