Pu-Edgeformer++: An Advanced Hierarchical Edge Transformer for Arbitrary-Scale Point Cloud Upsampling using Distance Fields

Despite of pre-processing the raw point cloud is important, limited research has been conducted on learning-based approaches to point cloud upsampling. PU-EdgeFormer [1] model stands out for its exceptional performance, which is attributed to its unique inductive biases that seamlessly blend both local and global characteristics of point clouds through the integration of graph convolution and transformer mechanisms. In this paper, we present EdgeFormer++, an advanced hierarchical edge transformer module designed for arbitrary-scale point cloud upsampling. Our module employs crossattention and dense connections to integrate information from both feature and input point cloud while maintaining the structural elements of graph convolutions and transformers. Experimental results, both qualitative and quantitative, indicate that our proposed method outperforms existing techniques in point cloud upsampling. The official source code is accessible at https://github.com/dohoon2045/PU-EdgeFormer2.