SS3D: A Semi-Supervised Learning Approach for Accurate Three-Dimensional Shape Reconstruction

The challenge of reconstructing three-dimensional (3D) models from images lies in how to infer a complete 3D structure with detailed geometry from two-dimensional (2D) images. However, single-view reconstruction requires only one image to reason about the 3D shape, demonstrating significant application potential. Yet, most existing methods rely on fully supervised learning approaches, which demand large amounts of labeled data. To alleviate this issue, semi-supervised learning strategies have been proposed to reduce the dependence on annotated data, offering a more efficient solution for single-view 3D reconstruction. We propose a semi-supervised single-view 3D point cloud reconstruction framework that employs a teacher-student paradigm to leverage limited labeled data together with abundant unlabeled samples. To address the modality heterogeneity between 2D images and 3D point clouds, a Heterogeneous Feature Attention Mechanism is designed to align cross-modal features and embed image cues into 3D spatial structures, preserving both geometric and appearance information. Moreover, a Self-Attention Decoder captures global dependencies and salient regions, enabling fine-grained structural recovery. Our model demonstrates outstanding performance on both the ShapeNet dataset and the Pix3D dataset, achieving an L1-Chamfer distance (CD) value of 5.60 × 10 − 2 on ShapeNet and an L1-CD value of 6.29 × 10 − 2 on Pix3D. Furthermore, rigorous ablation studies provide additional confirmation of the remarkable effectiveness of our approach.