Capturing Machine Learned 3D Foot Shapes from a Single Camera

在鞋类中，合脚决定了舒适度和性能，并且高度依赖于脚形，这并不完全取决于鞋码。基于RGBD相机的经济实惠的扫描仪可用于获取更详细的尺寸信息，并允许更个性化的鞋类匹配。在扫描物体时，通常需要来自不同视图的许多图像来重建整体形状，但是可以利用先验信息来更有效地重建模型并填充缺失的信息。深度学习方法已经被证明能够从家具和车辆等物体的有限输入中重建3D形状。这种方法可以应用于3D扫描，其中可以从单个输入视图形成完整的扫描。我们将深度学习方法应用于足部扫描，并提出了一种从单个输入深度图重建3D点云扫描的方法。与文献中研究的其他物体相比，拟人化的身体部位可能具有挑战性，因为它们的形状不规则、参数化困难和对称性有限。我们将利用从CAESAR数据集构建的MPII人体形状模型来训练基于视图合成的网络。我们将研究使用少到一个摄像头，RGB-D摄像头或仅RGB摄像头。

In footwear, fit determines comfort and performance, and is highly dependent on foot shape, something that is not fully captured by shoe size. Affordable scanners based on RGBD cameras can be used to acquire more detailed sizing information, and allow for more personalized footwear matching. When scanning an object, many images from different views are usually required to reconstruct the overall shape, however prior information can be leveraged to more efficiently recreate models and fill in missing information. Deep learning methods have been shown to be able to reconstruct 3D shape from limited inputs in objects such as furniture and vehicles. This approach can be applied in 3D scanning, where a complete scan can be formed from a single input view. We apply a deep learning approach to foot scanning, and present a method to reconstruct a 3D point cloud scan from a single input depth map. Anthropomorphic body parts can be challenging compared to other objects studied in literature due to their irregular shapes, difficulty for parameterizing and limited symmetries. We will leverage MPII Human Shape models built from the CAESAR dataset to train a view synthesis based network. We will investigate using as few as one camera, either a RGB-D camera or just a RGB camera.