DOI: 10.5061/dryad.rbnzs7hj5
title: 6DOF pose estimation - synthetically generated dataset using BlenderProc

description: Accurate and robust 6DOF (Six Degrees of Freedom) pose estimation is a
 critical task in various fields, including computer vision, robotics, and
 augmented reality. This research paper presents a novel approach to
 enhance the accuracy and reliability of 6DOF pose estimation by
 introducing a robust method for generating synthetic data and leveraging
 the ease of multi-class training using the generated dataset. The proposed
 method tackles the challenge of insufficient real-world annotated data by
 creating a large and diverse synthetic dataset that accurately mimics
 real-world scenarios. The proposed method only requires a CAD model of the
 object and there is no limit to the number of unique data that can be
 generated. Furthermore, a multi-class training strategy that harnesses the
 synthetic dataset's diversity is proposed and presented. This
 approach mitigates class imbalance issues and significantly boosts
 accuracy across varied object classes and poses. Experimental results
 underscore the method's effectiveness in challenging conditions,
 highlighting its potential for advancing 6DOF pose estimation across
 diverse applications. Our approach only uses a single RGB frame and is
 real-time.
description: This dataset has been synthetically generated using 3D software
 like Blender and APIs like Blendeproc.
description: # Data Repository README This repository contains data organized into a
 structured format. The data consists of three main folders and two files,
 each serving a specific purpose. The data contains two folders - Cat and
 Hand. Cat Dataset: 63492 labeled data with images, masks, and poses. Hand
 Dataset: 42418 labeled data with images, masks, and poses. Usage: The
 dataset is ready for use by simply extracting the contents of the zip
 file, whether for training in a segmentation task or a pose estimation
 task. To view .npy files you will need to use Python with the numpy
 package installed. In Python use the following commands. import numpy\
 data = numpy.load('file.npy')\ print(data) What free/open
 software is appropriate for viewing the .ply files?\ These files can be
 opened using any 3D modeling software like Blender, Meshlab, etc. Camera
 Matrix Intrinstics Format : Fx 0 px 0 Fy py 0 0 0 Below is an overview of
 the data organization: ## Folder Structure 1. **Rgb:** * This folder
 contains a collection of synthetically generated images. * Images are
 organized based on index numbers. * Example: `rgb/0.png`, `images/1.png`,
 ... 2. **Mask:** * This folder contains corresponding masks for the
 images. * Masks are also organized based on index numbers. * Example:
 `mask/0.png`, `masks/1.png`, ... 3. **Pose:** * The poses folder holds
 pose data associated with the images. * As with the images and masks,
 poses are also indexed. * Example: `pose/0.npy`, `pose/1.npy`, ... ##
 Files 1. **3D Model:** * This repository includes a 3D model file. * The
 3D model is named `model.ply`. 2. **Intrinsic Parameters:** * A text file
 named `camera.txt` is provided. * This file contains intrinsic parameters
 for the data. * Intrinsic parameters are essential for various computer
 vision tasks. ## Usage This following model can be directly used to train
 a 6-DOF Pose Estimation model using Clean-Pvnet ## Acknowledgments This
 dataset was created and processed using BlenderProc, a powerful tool for
 generating synthetic data in Blender. We would like to express our
 gratitude to the BlenderProc development team and community for their
 invaluable contribution to the generation and manipulation of 3D data. ##
 Citation If you use this dataset in your work, please consider citing
 BlenderProc as follows: M. Denninger et al., “BlenderProc: Reducing the
 Reality Gap with Photorealistic Rendering,” Journal of Open Source
 Software, Jul. 2020, [Online]. Available: