duality-robotics/pose_estimation

Pose Estimation Dataset

Overview

This dataset is designed for pose estimation tasks, focusing on determining the position and orientation of an object in 3D space. The dataset includes images, masks, and labels for both training and validation, making it suitable for machine learning applications in 3D object tracking and computer vision. This dataset was generated using Duality.ai simulation software: FalconEditor. Try FalconEditor today to create data to be used for pose estimation on different objects.

Dataset Structure

The dataset has the following structure:

pose_estimation_dataset/
|-- train/
|   |-- images/
|   |   |-- 000000000.png
|   |   |-- 000000001.png
|   |   |-- ...
|   |-- labels/
|   |   |-- 000000000.txt
|   |   |-- 000000001.txt
|   |   |-- ...
|   |-- masks/
|       |-- 000000000.png
|       |-- 000000001.png
|       |-- ...
|-- val/
    |-- images/
    |   |-- 000000000.png
    |   |-- 000000001.png
    |   |-- ...
    |-- labels/
    |   |-- 000000000.txt
    |   |-- 000000001.txt
    |   |-- ...
    |-- masks/
        |-- 000000000.png
        |-- 000000001.png
        |-- ...

Components

1. Images: RGB images of the object in .png format.
2. Labels: Text files (.txt) containing 3D pose annotations. Each label file corresponds to an image and contains the following information:
   • Three position values [x, y, z] representing the object's location in 3D space.
   • Four quaternion values [qx, qy, qz, qw] representing the object's orientation in 3D space.
3. Masks: Binary masks (.png) highlighting the object’s silhouette in the image.

Usage

To use this dataset, load the images, labels, and masks for your pose estimation pipeline. Ensure that the corresponding image, label, and mask files share the same base filename.

Example

If you have train/images/image_1.png, the corresponding files will be:

• train/labels/image_1.txt
• train/masks/image_1.png

Label Format

Each .txt label file contains a single line in the following format:

x y z qx qy qz qw

Example:

0.12 0.45 0.78 0.0 0.707 0.0 0.707

Licensing

license: apache-2.0