3DTNet: Learning Local Features Using 2D and 3D Cues

摘要

We present an approach to learn 3D local descriptor by combining both 2D texture and 3D geometric information, which can be used to register partial 3D data for a variety of vision applications. Unlike previous approaches which simply concatenate features learned from multiple sources into one feature descriptor, we learn 2D and 3D feature representations jointly. We design a network, 3DTNet with an architecture particularly designed for learning robust local feature representation leveraging both texture and geometric information. Two types of information are interacted with each other which results in more robust and stable feature representation. Finally, feature representations of multi-scale neighborhoods are aggregated to further improve the performance of feature matching. Extensive experimental results show that our method outperforms state-of-art 2D or 3D descriptors in terms of both accuracy and efficiency.