MetaSSP: Enhancing Semi-supervised Implicit 3D Reconstruction through Meta-adaptive EMA and SDF-aware Pseudo-label Evaluation
This addresses the scalability issue in implicit 3D reconstruction for computer vision applications, representing a strong specific gain rather than a foundational advancement.
The paper tackled the problem of high-quality single-view 3D reconstruction requiring large labeled datasets by proposing MetaSSP, a semi-supervised framework that uses unlabeled images, resulting in a 20.61% reduction in Chamfer Distance and a 24.09% increase in IoU on the Pix3D benchmark compared to baselines.
Implicit SDF-based methods for single-view 3D reconstruction achieve high-quality surfaces but require large labeled datasets, limiting their scalability. We propose MetaSSP, a novel semi-supervised framework that exploits abundant unlabeled images. Our approach introduces gradient-based parameter importance estimation to regularize adaptive EMA updates and an SDF-aware pseudo-label weighting mechanism combining augmentation consistency with SDF variance. Beginning with a 10% supervised warm-up, the unified pipeline jointly refines labeled and unlabeled data. On the Pix3D benchmark, our method reduces Chamfer Distance by approximately 20.61% and increases IoU by around 24.09% compared to existing semi-supervised baselines, setting a new state of the art.