  Point clouds are commonly used in various practical applications such as
autonomous driving and the manufacturing industry. However, these point clouds
often suffer from incompleteness due to limited perspectives, scanner
resolution and occlusion. Therefore the prediction of missing parts performs a
crucial task. In this paper, we propose a novel method for point cloud
completion. We utilize a spherical template to guide the generation of the
coarse complete template and generate the dynamic query tokens through a
correspondence pooling (Corres-Pooling) query generator. Specifically, we first
generate the coarse complete template by embedding a Gaussian spherical
template into the partial input and transforming the template to best match the
input. Then we use the Corres-Pooling query generator to refine the coarse
template and generate dynamic query tokens which could be used to predict the
complete point proxies. Finally, we generate the complete point cloud with a
FoldingNet following the coarse-to-fine paradigm, according to the fine
template and the predicted point proxies. Experimental results demonstrate that
our T-CorresNet outperforms the state-of-the-art methods on several benchmarks.
Our Codes are available at https://github.com/df-boy/T-CorresNet.
