A Novel GAN-Based Point Cloud Completion Network for 3D Object Enhancement

Three-dimensional (3D) point clouds are essential for representing traffic scenes in autonomous driving, yet incompletion often occurs due to sensor angle limitations and signal occlusions. Point cloud completion focuses on generating the missing parts of incomplete point cloud shapes, but still shows limitations in preserving and restoring the details of the shapes. To address these challenges, we propose a novel point cloud completion method, termed PCC-GAN, which leverages an improved generative adversarial network to predict and correct missing portions of point clouds. The proposed architecture features two primary components: a generator and a discriminator. The generator includes a feature extension module that uses local feature interpolation to learn and refine the input point cloud features, thereby enhancing learning efficiency. Meanwhile, the discriminator employs a self-attention mechanism to capture long-range dependencies and extract detailed contextual information, allowing it to evaluate the local accuracy of the generated features effectively. The performance of the PCC-GAN model is assessed using public datasets, and its robustness is tested across point cloud sets with varying levels of missing proportion. Results indicate that PCC-GAN significantly improves the shape reconstruction of lidar point clouds, demonstrating strong predictive capabilities and robustness.