Our work learns a unified model for single-view 3D reconstruction of objects from hundreds of semantic categories. As a scalable alternative to direct 3D supervision, our work relies on segmented image collections for learning 3D of generic categories. Unlike prior works that use similar supervision but learn independent category-specific models from scratch, our approach of learning a unified model simplifies the training process while also allowing the model to benefit from the common structure across categories. Using image collections from standard recognition datasets, we show that our approach allows learning 3D inference for over 150 object categories. We evaluate using two datasets and qualitatively and quantitatively show that our unified reconstruction approach improves over prior category-specific reconstruction baselines. Our final 3D reconstruction model is also capable of zero-shot inference on images from unseen object categories and we empirically show that increasing the number of training categories improves the reconstruction quality.

本研究提出了一种统一的模型，用于针对数百个语义类别的单视图 3D 对象重建，并利用分割的图像集合来学习分类通用物体的 3D 形状，通过使用标准识别数据集的图像集进行评估，表明该方法使得对150个以上物体类别的3D推断成为可能。最终得到的 3D 重建模型还能够对未见物体类别的图像进行零样本推断，并且通过实验证明，增加训练类别的数量可以提高重建质量。

预训练、自训练、蒸馏：三维重建超强增强的简单配方