Despite the recent development of deep learning-based point cloud upsampling,
most MLP-based point cloud upsampling methods have limitations in that it is
difficult to train the local and global structure of the point cloud at the
same time. To solve this problem, we present a combined graph convolution and
transformer for point cloud upsampling, denoted by PU-EdgeFormer. The proposed
method constructs EdgeFormer unit that consists of graph convolution and
multi-head self-attention modules. We employ graph convolution using EdgeConv,
which learns the local geometry and global structure of point cloud better than
existing point-to-feature method. Through in-depth experiments, we confirmed
that the proposed method has better point cloud upsampling performance than the
existing state-of-the-art method in both subjective and objective aspects. The
code is available at this https URL

本研究提出了一种基于图卷积和变压器结合的点云上采样方法 PU-EdgeFormer，能够同时学习点云的局部和全局结构，通过实验证明在客观和主观方面都优于现有的最先进方法。

PU-EdgeFormer: 点云稠密预测的边缘 Transformer 上采样

PU-EdgeFormer: Edge Transformer for Dense Prediction in Point Cloud  Upsampling

We introduce EdgeFormer -- a parameter-efficient Transformer for on-device
seq2seq generation under the strict computation and memory constraints.
Compared with the previous parameter-efficient Transformers, EdgeFormer applies
two novel principles for cost-effective parameterization, allowing it to
perform better given the same parameter budget; moreover, EdgeFormer is further
enhanced by layer adaptation innovation that is proposed for improving the
network with shared layers.
Extensive experiments show EdgeFormer can effectively outperform previous
parameter-efficient Transformer baselines and achieve competitive results under
both the computation and memory constraints. Given the promising results, we
release EdgeLM -- the pretrained version of EdgeFormer, which is the first
publicly available pretrained on-device seq2seq model that can be easily
fine-tuned for seq2seq tasks with strong results, facilitating on-device
seq2seq generation in practice.

EdgeFormer 是一种面向严格计算和内存限制下的设备本地序列生成的小型 Transformer，应用了两个新颖的成本效益参数化原则，扩展了共享层的网络，并通过大量实验达到了竞争优势。提出了 EdgeLM，这是第一个可供公开使用的在设备上预训练的序列到序列模型，可在实践中促进设备本地序列生成并取得良好结果。