A Hierarchical Attention Fused Descriptor for 3D Point Matching

A Hierarchical Attention Fused Descriptor for 3D Point Matching

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Motivated by recent successes on learning 3D feature representations, we present a Siamese network to generate representative 3D descriptors for Dog Tag Necklace Gift Box 3D point matching in point cloud registration.Our system, dubbed HAF-Net, consists of feature extraction module, hierarchical feature reweighting and recalibration module (HRR), as well as feature aggregation and compression module.The HRR module is proposed to adaptively integrate multi-level features through learning, acting as a hierarchical attention fusion mechanism.The learnable feature pooling technique VLAD is extended into our aggregation module, which is further utilized to extract principal components of features and compress them into a low dimensional feature vector.

To train our model, we amass a large dataset for 3D point matching.The dataset is composed of matched Twin Sleigh Bed w/Storage and unmatched point block pairs, which are automatically searched from existing reconstruction datasets with known poses.The experiments demonstrate that the proposed HAF-Net not only outperforms other state-of-the-art approaches in 3D feature representation but also has a good generalization ability in various tasks and datasets.