A Method to Generate High Precision Mesh Model and RGB-D Datasetfor 6D Pose Estimation Task

TL;DR

This paper introduces a new method for generating high-precision mesh models and RGB-D datasets to improve 6D pose estimation, addressing limitations of outdated sensors and enhancing data quality for deep learning.

Contribution

A novel object reconstruction method that produces more accurate, high-resolution datasets closely resembling synthetic data, filling a gap in current 3D vision datasets.

Findings

Produced datasets with higher resolution and accuracy

Reduced gap between real and synthetic data

Improved 6D pose estimation performance

Abstract

Recently, 3D version has been improved greatly due to the development of deep neural networks. A high quality dataset is important to the deep learning method. Existing datasets for 3D vision has been constructed, such as Bigbird and YCB. However, the depth sensors used to make these datasets are out of date, which made the resolution and accuracy of the datasets cannot full fill the higher standards of demand. Although the equipment and technology got better, but no one was trying to collect new and better dataset. Here we are trying to fill that gap. To this end, we propose a new method for object reconstruction, which takes into account the speed, accuracy and robustness. Our method could be used to produce large dataset with better and more accurate annotation. More importantly, our data is more close to the rendering data, which shrinking the gap between the real data and synthetic data further.