HFF-Net: Hierarchical Feature Fusion Network for Point Cloud Generation with Point Transformers

Abstract

Estimating the complete 3D point cloud from a partial input is a key challenge in 3D vision. Existing point cloud completion networks overlook the long-range, hierarchical features and object details of the incomplete point cloud. To this end, we propose Hierarchical Feature Fusion Network (HFF-Net) for precise and detailed point cloud completion. To succeed at this task, HFF-Net estimates the missing Point Agents (PAs) by designing a topology-aware transformer-based encoder-decoder network with Multi-level Feature Learning (MFL), which hierarchically exploits the various regional and detailed information. Further, to make better utilization of the hierar-chical information captured from MFL, we devise the Hier-archical Features Fusion (HFF) module to convert them into cross-regional features. Besides, the predicted PAs is utilized by a multi-resolution output module to recover the missing point cloud in a coarse-to-fine manner. Experiments indi-cate that HFF-Net performs favorably against state-of-the-art (SOTA) approaches on both the new-proposed and existing datasets.