QINet: Decision Surface Learning and Adversarial Enhancement for Quasi-Immune Completion of Diverse Corrupted Point Clouds

Abstract

In point cloud completion task, most previous works fail to deal with diverse corrupted point clouds with large missing areas. Meanwhile, they are restricted by discrete point clouds lacking smooth surfaces to represent an object, and the resolution of generated point clouds is fixed once their networks are determined. In addition, the evaluation metrics are not specific for this task. Thus, we propose an innovative quasi-immune completion architecture of point cloud called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QINet</i> in this paper, which is inspired by the artificial immunization process in biology. Specifically, to increase robustness and adaptation of the model, we conceive a mask algorithm named onion-peeling to generate diverse corrupted inputs. Meanwhile, two proposed modules are combined together to produce flexible resolution of point clouds, namely the decision surface learning and adversarial enhancement for the latent representation recovery. The first module transforms point clouds to surfaces with a continuous decision boundary function, which the second module is applied to deduce complete surface from corrupted point cloud by the cooperation of reinforcement learning and latent generative adversarial network. Besides, we evaluate the shortcomings of the existing methods and present two novel metrics to support multi-faceted comparisons. Experimental results verify that our approach can generate continuous 3D shapes with optional resolutions compared to other approaches, and achieves competitive results both quantitatively and qualitatively.