A Method to Generate High Precision Mesh Model and RGB-D Datasetfor 6D Pose Estimation Task
This work is incrementally significant for researchers and developers in 3D vision and deep learning who require higher precision datasets for 6D pose estimation.
The paper addresses the lack of high-quality datasets for 6D pose estimation by proposing a new method for object reconstruction. This method aims to generate large datasets with improved accuracy and annotations, bridging the gap between real and synthetic data.
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 However, the depth sensors used to make these datasets are out of date, which made the resolution and accuracy of the datasets cannot full fill the higher standards of demand.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.