  To further promote the development of multimodal point cloud completion, we
contribute a large-scale multimodal point cloud completion benchmark
ModelNet-MPC with richer shape categories and more diverse test data, which
contains nearly 400,000 pairs of high-quality point clouds and rendered images
of 40 categories. Besides the fully supervised point cloud completion task, two
additional tasks including denoising completion and zero-shot learning
completion are proposed in ModelNet-MPC, to simulate real-world scenarios and
verify the robustness to noise and the transfer ability across categories of
current methods. Meanwhile, considering that existing multimodal completion
pipelines usually adopt a unidirectional fusion mechanism and ignore the shape
prior contained in the image modality, we propose a Dual-Modality Feature
Interaction Network (DuInNet) in this paper. DuInNet iteratively interacts
features between point clouds and images to learn both geometric and texture
characteristics of shapes with the dual feature interactor. To adapt to
specific tasks such as fully supervised, denoising, and zero-shot learning
point cloud completions, an adaptive point generator is proposed to generate
complete point clouds in blocks with different weights for these two
modalities. Extensive experiments on the ShapeNet-ViPC and ModelNet-MPC
benchmarks demonstrate that DuInNet exhibits superiority, robustness and
transfer ability in all completion tasks over state-of-the-art methods. The
code and dataset will be available soon.
