用多形状先验与自注意力提升少标签3D重建质量
Semi-supervised Single-view 3D Reconstruction via Multi Shape Prior Fusion Strategy and Self-Attention
- 融合多个形状先验引导结构生成
- 在1%标签下性能超越监督方法3.3%
- 适合数据稀缺场景的3D重建任务
单视图3D重建传统依赖昂贵的3D标注数据。为降低对标注数据的依赖，本文提出一种半监督框架，创新性引入多形状先验融合策略，以生成更逼真的物体结构。同时，在传统解码器中集成自注意力模块以提升生成质量。在ShapeNet数据集上，该方法在1%、10%、20%标签比例下均显著优于现有监督学习方法，相比基线提升3.3%。在真实世界数据集Pix3D上也表现优异。全面消融实验验证了方法的有效性。代码已开源。
原文摘要 · Abstract (English)
In the domain of single-view 3D reconstruction, traditional techniques have frequently relied on expensive and time-intensive 3D annotation data. Facing the challenge of annotation acquisition, semi-supervised learning strategies offer an innovative approach to reduce the dependence on labeled data. Despite these developments, the utilization of this learning paradigm in 3D reconstruction tasks remains relatively constrained. In this research, we created an innovative semi-supervised framework for 3D reconstruction that distinctively uniquely introduces a multi shape prior fusion strategy, intending to guide the creation of more realistic object structures. Additionally, to improve the quality of shape generation, we integrated a self-attention module into the traditional decoder. In benchmark tests on the ShapeNet dataset, our method substantially outperformed existing supervised learning methods at diverse labeled ratios of 1\%, 10\%, and 20\%. Moreover, it showcased excellent performance on the real-world Pix3D dataset. Through comprehensive experiments on ShapeNet, our framework demonstrated a 3.3\% performance improvement over the baseline. Moreover, stringent ablation studies further confirmed the notable effectiveness of our approach. Our code has been released on https://github.com/NWUzhouwei/SSMP
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