Brain-inspired Recurrent Illusory Contour Encoder for Point Cloud Completion

Point cloud completion refers to the process of recovering a complete structural representation from incomplete or sparse 3D point cloud data. Currently, mainstream point cloud completion methods are unanimously based on deep learning architectures. On one hand, these methods tend to repetitively stacking complex attention modules to enhance network performance, which drifts away from the neat design of unified and interpretable networks; on the other hand, nearly all existing architectures rely solely on feedforward connections to propagate information, which lacks feedback and supervision from deeper layers. The recurrent neural architecture, formed by feedforward-feedback loops, have shown its ability to generate Illusory Contours (ICs) in the V1 region of brains from several species of mammals; and ICs (as exemplified by the Kanizsa triangle) are essentially a completion mechanism of the brain for visual information. Inspired by this observation, we design a Brain-inspired Recurrent Illusory Contour Encoder (BRICE) to “imagine” what is missing in the incomplete 3D point clouds. Building upon the foundation model of Predictive Coding (PC), the plug-and-play nature enables it to be seamlessly integrated into mainstream point cloud completion networks. Experimental results have shown that BRICE can enhance various point cloud completion baselines on 3D datasets such as MVP2K, PCN, and PlantPCom.