论文标题
一种生成高精度网格模型和RGB-D数据集的方法6D姿势估计任务
A Method to Generate High Precision Mesh Model and RGB-D Datasetfor 6D Pose Estimation Task
论文作者
论文摘要
最近，由于深度神经网络的发展，3D版本得到了极大的改进。高质量的数据集对深度学习方法很重要。已经构建了用于3D视觉的现有数据集，例如Bigbird和YCB。但是，用于使这些数据集的深度传感器已过时，这使数据集的分辨率和准确性无法完全填充更高的需求标准。尽管设备和技术变得更好，但是没有人试图收集新的，更好的数据集。在这里，我们试图填补这一空白。为此，我们提出了一种新的对象重建方法，该方法考虑了速度，准确性和稳健性。我们的方法可用于生产具有更好，更准确的注释的大型数据集。更重要的是，我们的数据更接近渲染数据，这会进一步缩小真实数据和合成数据之间的差距。
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 However, the depth sensors used to make these datasets are out of date, which made the resolution and accuracy of the datasets cannot full fill the higher standards of demand.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.
