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.

本论文提出了一种有效的点云生成方法，从潜在向量中生成相同形状的多分辨率点云；使用新颖的渐进去卷积网络和基于学习的双边插值，从低分辨率点云开始，通过级联的去卷积层逐渐输出高分辨率的局部和全局特征 maps，使用多层感知器作为生成网络生成多分辨率点云，并提出了形状保持对抗损失来训练点云去卷积生成网络，实验结果表明了方法的有效性。

渐进式点云解卷积生成网络

Progressive Point Cloud Deconvolution Generation Network

We propose a novel semantic segmentation algorithm by learning a
deconvolution network. We learn the network on top of the convolutional layers
adopted from VGG 16-layer net. The deconvolution network is composed of
deconvolution and unpooling layers, which identify pixel-wise class labels and
predict segmentation masks. We apply the trained network to each proposal in an
input image, and construct the final semantic segmentation map by combining the
results from all proposals in a simple manner. The proposed algorithm mitigates
the limitations of the existing methods based on fully convolutional networks
by integrating deep deconvolution network and proposal-wise prediction; our
segmentation method typically identifies detailed structures and handles
objects in multiple scales naturally. Our network demonstrates outstanding
performance in PASCAL VOC 2012 dataset, and we achieve the best accuracy
(72.5%) among the methods trained with no external data through ensemble with
the fully convolutional network.

本文提出一种基于学习反卷积网络的新颖语义分割算法，其中反卷积网络由反卷积层和上采样层组成，能够在像素级别上识别图像中的类别标签和预测分割掩模，通过将结果合并得到最终的分割图像，并且可以自然地处理多尺度下的物体和识别细节结构，在 PASCAL VOC 2012 数据集上进行实验，通过与完全卷积网络的集合学习，取得了最好的 72.5% 精度。