DenseConv for 3D point set upsampling

Abstract

Abstract Recently, a data‐driven approach from point cloud upsampling network (PU‐Net) has been used to upsample point set, making it from sparse to dense. However, PU‐Net first needs to downsample the point set before upsampling, and extract the point features of fixed‐level in the sampling area. The local features are not enough to extract, and then restoring its global representation will lead to point cloud coordinates to be inaccurate. In view of the insufficient local feature extraction, EdgeConv module is utilised to merge the points and the information in the local neighbourhood. In addition, a dense connection is proposed to maintain the effective transmission of information across the entire network. In this letter, EdgeConv and dense connection are combined into DenseConv that are nested for recursive use on PU‐Net to ensure the accuracy of the generation of point cloud. The experimental results show that the average error of the proposed method is reduced by about 5% on the PU‐Net dataset and about 10% on the ModelNet10 dataset, which verifies the effectiveness of the proposed method.

Keywords

• Graphics techniques
• Combinatorial mathematics
• Neural nets