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OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation (CVPR 2022)

<p align="center"> <img src ="assets/introduction_figure.png" width="500" /> </p>
@inproceedings{cai2022ove6d,
  title={OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation},
  author={Cai, Dingding and Heikkil{\"a}, Janne and Rahtu, Esa},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6803--6813},
  year={2022}
}

Setup

Please start by installing Miniconda3 with Pyhton3.8 or above.

Denpendencies

This project requires the evaluation code from bop_toolkit and sixd_toolkit.

Dataset

Our evaluation is conducted on three datasets all downloaded from BOP website. All three datasets are stored in the same directory. e.g. Dataspace/lm, Dataspace/lmo, Dataspace/tless.

Quantitative Evaluation

Evaluation on the LineMOD and Occluded LineMOD datasets with instance segmentation (Mask-RCNN) network (entire pipeline i.e. instance segmentation + pose estimation)

python LM_RCNN_OVE6D_pipeline.py for LineMOD.

python LMO_RCNN_OVE6D_pipeline.py for Occluded LineMOD.

Evaluation on the T-LESS dataset with the provided object segmentation masks (downloaded from Multi-Path Encoder).

python TLESS_eval_sixd17.py for TLESS.

Training

To train OVE6D, the ShapeNet dataset is required. You need to first pre-process ShapeNet with the provided script in training/preprocess_shapenet.py, and Blender is required for this task. More details refer to LatentFusion.

pre-trained weight for OVE6D

Our pre-trained OVE6D weights can be found here. Please download and save to the directory checkpoints/.

Segmentation Masks

<!-- - 1. For T-LESS we use the [segmentation masks](https://dlrmax.dlr.de/get/c677b2a7-78cf-5787-815b-7ba2c26555a7/) provided by [Multi-Path Encoder](https://github.com/DLR-RM/AugmentedAutoencoder/tree/multipath). -->

Acknowledgement