论文标题
3D点云的学习几何图像表示
Learning geometry-image representation for 3D point cloud generation
论文作者
论文摘要
我们研究生成3D对象的点云的问题。我们提出了一个基于几何形象的生成器（GIG）将对象离散为具有巨大的计算成本和分辨率限制的3D体素，以将3D点云生成问题转换为2D几何形象图像生成问题。由于几何图像是一个完全常规的2D阵列，其中包含3D对象的表面点，因此它既利用2D阵列的规律性，又利用3D表面的地球邻域的规律性。因此，我们的演出的一个重要好处是，它允许我们使用高效的2D图像生成网络直接生成3D点云。刚性和非刚性3D对象数据集的实验证明了我们方法的有希望的性能，不仅可以创建合理和新颖的3D对象，而且还学习一个概率的潜在空间，可以很好地支持形状编辑，例如插值和算术。
We study the problem of generating point clouds of 3D objects. Instead of discretizing the object into 3D voxels with huge computational cost and resolution limitations, we propose a novel geometry image based generator (GIG) to convert the 3D point cloud generation problem to a 2D geometry image generation problem. Since the geometry image is a completely regular 2D array that contains the surface points of the 3D object, it leverages both the regularity of the 2D array and the geodesic neighborhood of the 3D surface. Thus, one significant benefit of our GIG is that it allows us to directly generate the 3D point clouds using efficient 2D image generation networks. Experiments on both rigid and non-rigid 3D object datasets have demonstrated the promising performance of our method to not only create plausible and novel 3D objects, but also learn a probabilistic latent space that well supports the shape editing like interpolation and arithmetic.
