论文标题
立体声摄像头的端到端3D手姿势估算
End-to-End 3D Hand Pose Estimation from Stereo Cameras
论文作者
论文摘要
这项工作提出了一种端到端的方法，以估算立体声相机的完整3D手姿势。从立体相机估算手姿势的大多数现有方法都采用立体声匹配来获得深度图并使用基于深度的解决方案来估计手工姿势。相比之下，我们建议绕过立体声匹配，并直接估计立体声图像对的3D手姿势。提出的神经网络结构从任何关键点预测变量延伸，以估计手关节的稀疏差异。为了有效地训练模型，我们提出了一个由立体图像对和地面真相3D手姿势注释组成的大型合成数据集。实验表明，所提出的方法的表现优于基于立体深度的现有方法。
This work proposes an end-to-end approach to estimate full 3D hand pose from stereo cameras. Most existing methods of estimating hand pose from stereo cameras apply stereo matching to obtain depth map and use depth-based solution to estimate hand pose. In contrast, we propose to bypass the stereo matching and directly estimate the 3D hand pose from the stereo image pairs. The proposed neural network architecture extends from any keypoint predictor to estimate the sparse disparity of the hand joints. In order to effectively train the model, we propose a large scale synthetic dataset that is composed of stereo image pairs and ground truth 3D hand pose annotations. Experiments show that the proposed approach outperforms the existing methods based on the stereo depth.
