论文标题
渐进点云反卷积生成网络
Progressive Point Cloud Deconvolution Generation Network
论文作者
论文摘要
在本文中，我们提出了一种有效的点云生成方法，该方法可以从潜在向量产生相同形状的多分辨率点云。具体而言，我们通过基于学习的双边插值开发了一个新型的渐进卷积网络。基于学习的双边插值在点云的空间和特征空间中进行，以便可以利用点云的局部几何结构信息。从低分辨率点云开始，通过双边插值和最大功能操作，反卷积网络可以逐步输出高分辨率的本地和全局特征图。通过串联本地和全局特征图的不同分辨率，我们使用多层感知器作为生成网络来生成多分辨率点云。为了保持点云的不同分辨率的形状一致，我们提出了一种具有形状的对抗性损失来训练点云反卷积生成网络。实验结果证明了我们提出的方法的有效性。
In this paper, we propose an effective point cloud generation method, which can generate multi-resolution point clouds of the same shape from a latent vector. Specifically, we develop a novel progressive deconvolution network with the learning-based bilateral interpolation. The learning-based bilateral interpolation is performed in the spatial and feature spaces of point clouds so that local geometric structure information of point clouds can be exploited. Starting from the low-resolution point clouds, with the bilateral interpolation and max-pooling operations, the deconvolution network can progressively output high-resolution local and global feature maps. By concatenating different resolutions of local and global feature maps, we employ the multi-layer perceptron as the generation network to generate multi-resolution point clouds. In order to keep the shapes of different resolutions of point clouds consistent, we propose a shape-preserving adversarial loss to train the point cloud deconvolution generation network. Experimental results demonstrate the effectiveness of our proposed method.
