We address a 3D human pose estimation for equirectangular images taken by a wearable omnidirectional camera. The equirectangular image is distorted because the omnidirectional camera is attached closely in front of a person’s neck. Furthermore, some parts of the body are disconnected on the image; for instance, when a hand goes out to an edge of the image, the hand comes in from another edge. The distortion and disconnection of images make 3D pose estimation challenging. To overcome this difficulty, we introduce the location-maps method proposed by Mehta et al.; however, the method was used to estimate 3D human poses only for regular images without distortion and disconnection. We focus on a characteristic of the location-maps that can extend 2D joint locations to 3D positions with respect to 2D-3D consistency without considering kinematic model restrictions and optical properties. In addition, we collect a new dataset that is composed of equirectangular images and synchronized 3D joint positions for training and evaluation. We validate the location-maps’ capability to estimate 3D human poses for distorted and disconnected images. We propose a new location-maps-based model by replacing the backbone network with a state-of-the-art 2D human pose estimation model (HRNet). Our model is a simpler architecture than the reference model proposed by Mehta et al. Nevertheless, our model indicates better performance with respect to accuracy and computation complexity. Finally, we analyze the location-maps method from two perspectives: the map variance and the map scale. Therefore, some location-maps characteristics are revealed that (1) the map variance affects robustness to extend 2D joint locations to 3D positions for the 2D estimation error, and (2) the 3D position accuracy is related to the 2D locations relative accuracy to the map scale.

中文翻译：

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使用位置图的3D人体姿势估计模型，通过可穿戴式全向摄像机变形和断开图像

我们针对可穿戴式全向相机拍摄的等角矩形图像解决3D人体姿势估计问题。由于全向摄像机紧贴在人的脖子前面，因此等矩形图像失真。此外，身体的某些部位在图像上断开了连接。例如，当一只手伸出图像的边缘时，那只手从另一条边缘进入。图像的失真和断开使3D姿势估计具有挑战性。为了克服这个困难，我们引入了Mehta等人提出的位置图方法。但是，该方法仅用于估计正常图像的3D人体姿势，而不会失真和断开。我们专注于位置图的特征，该特征可以将2D关节位置相对于2D-3D一致性扩展到3D位置，而无需考虑运动学模型限制和光学特性。此外，我们收集了一个新的数据集，该数据集由等矩形图像和同步的3D关节位置组成，用于训练和评估。我们验证了位置图估计扭曲和断开图像的3D人体姿势的能力。通过用最新的2D人体姿势估计模型（HRNet）替代骨干网络，我们提出了一种新的基于位置图的模型。我们的模型比Mehta等人提出的参考模型更简单。然而，我们的模型在准确性和计算复杂性方面显示出更好的性能。最后，我们从两个角度分析了位置地图方法：地图差异和地图比例。因此，揭示了一些位置图特征：（1）地图方差会影响将2D关节位置扩展到2D估计误差的3D位置的鲁棒性；（2）3D位置精度与2D位置相对于2D位置的相对精度有关。地图比例尺。