Ear3D-PAF: PCA guided Adaptive Fusion Network for 3D Ear Point Cloud Reconstruction
Hebin Zhou, Li Yuan, Li Liu, Kang-Hyun Jo, Yanan Zhao · 2025
This study presents the Ear3D-PAF network, an advanced method for 3D ear point cloud reconstruction, addressing the challenges of data scarcity and complex structural geometry. The approach employs a PCA-guided encoder-decoder architecture to ensure global geometric coherence and high-fidelity reconstruction of local details. By synergistically integrating PCA guidance with a Point Cloud Encoder-Decoder framework, the encoder effectively captures both global and local feature representations. A Curvature-based Adaptive Feature Fusion mechanism enables the decoder to proficiently learn intricate ear geometries. In this strategy, high-curvature regions prioritize features derived from deep learning, while low-curvature regions emphasize PCA-guided features. Geometric consistency is optimized through a composite loss function incorporating Chamfer distance, mean squared error, and normal vector cosine distance. Evaluated on a dataset of 500 ear samples, the proposed method outperforms a PCA with 20 principal components, reducing Chamfer distance by 25.4% and mean squared error by 62%. Compared to PointNet++, it achieves reductions of 57% and 93.9%, respectively. The method exhibits superior reconstruction accuracy in high curvature areas, such as the helix and concavities, providing a robust and precise 3D reconstruction framework for applications in medical diagnostics, biometric authentication, and virtual reality.