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 …

Pairwise frame registration with sparse geometric local features on real-world depth images
is not particularly robust due to the low resolution and incomplete nature of the 3D scan
data. Moreover, there might be many regions with similar geometric information. In this
paper, we present 3DTDesc, a data-driven descriptor which closely combines both 2D
texture and 3D geometric information for frame registration. The proposed descriptor is
learned directly from color point clouds, which is time-efficient and provides robust and …