  In this paper we study the task of a single-view image-guided point cloud
completion. Existing methods have got promising results by fusing the
information of image into point cloud explicitly or implicitly. However, given
that the image has global shape information and the partial point cloud has
rich local details, We believe that both modalities need to be given equal
attention when performing modality fusion. To this end, we propose a novel
dual-channel modality fusion network for image-guided point cloud
completion(named DMF-Net), in a coarse-to-fine manner. In the first stage,
DMF-Net takes a partial point cloud and corresponding image as input to recover
a coarse point cloud. In the second stage, the coarse point cloud will be
upsampled twice with shape-aware upsampling transformer to get the dense and
complete point cloud. Extensive quantitative and qualitative experimental
results show that DMF-Net outperforms the state-of-the-art unimodal and
multimodal point cloud completion works on ShapeNet-ViPC dataset.
