SurgRIPE Challenge Submission

An improvement of PVNet for participating in the Surgical Robot Instrument Pose Estimation (SurgRIPE) challenge, which is a part of the Structured description of the challenge design of the Endoscopic Vision Challenge during MICCAI 23 in Vancouver.

This work is base on PVNet.

PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
Sida Peng, Yuan Liu, Qixing Huang, Xiaowei Zhou, Hujun Bao
CVPR 2019 oral
Project Page

Any questions or discussions are welcomed!

Improvements over Original PVNet

• Data Augmentation: Additional data augmentation were employed, which increases the robustness during occlusions.
• Hyperparameters & Settings: Modifications were made to certain hyperparameters and settings, optimizing them for the challenge.

Installation

Set up the environment with docker. See this.

Testing

Run the command below, and the script will automatically download the preprocessing files and weight files.

1. Prepare the data:

   python run.py --func preprocess --path /home/clean-pvnet/Dataset/[MBF/LND]/[TEST/TEST_OCC] --type [m/l]
   

2. Test:

   python run.py --func evaluate --path /home/clean-pvnet/Dataset/[MBF/LND]/[TEST/TEST_OCC] --type [m/l]
   

Data structure:

Organize the dataset as the following structure:

    ├── /path/to/dataset
    │   ├── model.ply
    │   ├── camera.txt
    │   ├── diameter.txt  
    │   ├── rgb/
    │   │   ├── 0.jpg
    │   │   ├── ...
    │   │   ├── 1234.jpg
    │   │   ├── ...
    │   ├── mask/
    │   │   ├── 0.png
    │   │   ├── ...
    │   │   ├── 1234.png
    │   │   ├── ...
    │   ├── pose/
    │   │   ├── pose0.npy
    │   │   ├── ...
    │   │   ├── pose1234.npy
    │   │   ├── ...
    │   │   └──