We propose deep virtual markers, a framework for estimating dense and accurate positional information for various types of 3D data. We design a concept and construct a framework that maps 3D points of 3D articulated models, like humans, into virtual marker labels. To realize the framework, we adopt a sparse convolutional neural network and classify 3D points of an articulated model into virtual marker labels. We propose to use soft labels for the classifier to learn rich and dense interclass relationships based on geodesic distance. To measure the localization accuracy of the virtual markers, we test FAUST challenge, and our result outperforms the state-of-the-art. We also observe outstanding performance on the generalizability test, unseen data evaluation, and different 3D data types (meshes and depth maps). We show additional applications using the estimated virtual markers, such as non-rigid registration, texture transfer, and realtime dense marker prediction from depth maps.
翻译:我们提出了深度虚拟标记,这是估算各种类型三维数据密度和准确位置信息的框架。我们设计了一个概念并构建了一个框架,将3D的3D点像人类一样的3D清晰模型绘制成虚拟标记标签。为了实现这一框架,我们采用了一个稀疏的进化神经网络,并将一个3D点的3D点划为虚拟标记标签。我们提议为分类者使用软标签,学习基于大地测量距离的丰富和密集的阶级关系。为了测量虚拟标记的本地精确度,我们测试FAUST的挑战,以及我们的结果超越了艺术的状态。我们还观察了通用测试、隐蔽数据评估和不同的3D数据类型(米什和深度地图)方面的杰出表现。我们展示了使用估计的虚拟标记的额外应用,例如非硬化登记、质素传输以及从深度地图上实时密集的标记预测。