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Point clouds captured by scanning devices are often incomplete due toocclusion. To overcome this limitation, point cloud completion methods havebeen developed to predict the complete shape of an object based on its partialinput. These methods can be broadly classified as supervised or unsupervised.However, both categories require a large number of 3D complete point clouds,which may be difficult to capture. In this paper, we propose Cross-PCC, anunsupervised point cloud completion method without requiring any 3D completepoint clouds. We only utilize 2D images of the complete objects, which areeasier to capture than 3D complete and clean point clouds. Specifically, totake advantage of the complementary information from 2D images, we use asingle-view RGB image to extract 2D features and design a fusion module to fusethe 2D and 3D features extracted from the partial point cloud. To guide theshape of predicted point clouds, we project the predicted points of the objectto the 2D plane and use the foreground pixels of its silhouette maps toconstrain the position of the projected points. To reduce the outliers of thepredicted point clouds, we propose a view calibrator to move the pointsprojected to the background into the foreground by the single-view silhouetteimage. To the best of our knowledge, our approach is the first point cloudcompletion method that does not require any 3D supervision. The experimentalresults of our method are superior to those of the state-of-the-artunsupervised methods by a large margin. Moreover, our method even achievescomparable performance to some supervised methods. We will make the source codepublicly available at https://github.com/ltwu6/cross-pcc.

Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion