  Recently, 3D shape understanding has achieved significant progress due to the
advances of deep learning models on various data formats like images, voxels,
and point clouds. Among them, point clouds and multi-view images are two
complementary modalities of 3D objects and learning representations by fusing
both of them has been proven to be fairly effective. While prior works
typically focus on exploiting global features of the two modalities, herein we
argue that more discriminative features can be derived by modeling ``where to
fuse''. To investigate this, we propose a novel Locality-Aware Point-View
Fusion Transformer (LATFormer) for 3D shape retrieval and classification. The
core component of LATFormer is a module named Locality-Aware Fusion (LAF) which
integrates the local features of correlated regions across the two modalities
based on the co-occurrence scores. We further propose to filter out scores with
low values to obtain salient local co-occurring regions, which reduces
redundancy for the fusion process. In our LATFormer, we utilize the LAF module
to fuse the multi-scale features of the two modalities both bidirectionally and
hierarchically to obtain more informative features. Comprehensive experiments
on four popular 3D shape benchmarks covering 3D object retrieval and
classification validate its effectiveness.
