We propose a novel convolutional operator for the task of point cloud
completion. One striking characteristic of our approach is that, conversely to
related work it does not require any max-pooling or voxelization operation.
Instead, the proposed operator used to learn the point cloud embedding in the
encoder extracts permutation-invariant features from the point cloud via a
soft-pooling of feature activations, which are able to preserve fine-grained
geometric details. These features are then passed on to a decoder architecture.
Due to the compression in the encoder, a typical limitation of this type of
architectures is that they tend to lose parts of the input shape structure. We
propose to overcome this limitation by using skip connections specifically
devised for point clouds, where links between corresponding layers in the
encoder and the decoder are established. As part of these connections, we
introduce a transformation matrix that projects the features from the encoder
to the decoder and vice-versa. The quantitative and qualitative results on the
task of object completion from partial scans on the ShapeNet dataset show that
incorporating our approach achieves state-of-the-art performance in shape
completion both at low and high resolutions.

我们提出了一种新的卷积算子，用于点云完成的任务。我们的方法的一个显著特点是与相关工作相反，它不需要任何最大池化或体素化操作。我们的算子不断提取具有置换不变性的特征，以保留细粒度的几何细节，通过对特征激活的软池化，从而学习编码器中的点云嵌入。通过特定设计的跳跃连接，将对应层之间的链接建立在编码器和解码器之间，以克服这类结构常见的限制，同时在点云完成任务中达到了最佳性能。