论文标题
基于斑块的人体细分
Deep Patch-based Human Segmentation
论文作者
论文摘要
3D人类分割在重分年内取得了明显的进步。但是，迄今为止仍然是一个挑战。在本文中，Weindroddroded是一种基于3D人体分割的深斑点方法。首先要为每个顶点提取局部表面贴片，然后将参数imeigit提取到2D网格（或图像）中。然后，我们嵌入了鉴定的形状描述2D网格，这些网格进一步馈送到功能强大的2D循环神经网络中，以回归相应的语义标签（例如，头部，躯干）。实验表明，我们的方法是有效的非人类分割，并达到了最先进的准确性。
3D human segmentation has seen noticeable progress in re-cent years. It, however, still remains a challenge to date. In this paper, weintroduce a deep patch-based method for 3D human segmentation. Wefirst extract a local surface patch for each vertex and then parameterizeit into a 2D grid (or image). We then embed identified shape descriptorsinto the 2D grids which are further fed into the powerful 2D Convolu-tional Neural Network for regressing corresponding semantic labels (e.g.,head, torso). Experiments demonstrate that our method is effective inhuman segmentation, and achieves state-of-the-art accuracy.
