GSIR: Generalizable 3D Shape Interpretation and Reconstruction

Jianren Wang, Zhaoyuan Fang ;


Single image 3D shape interpretation and reconstruction are closely related to each other but have long been studied separately and often end up with priors that are highly biased by training classes. Here we present an algorithm extit{(GSIR)}, designed to joint learning these two tasks to capture generic, class-agnostic shape priors for a better understanding of 3D geometry. We propose to recover 3D shape structures as cuboids from partially reconstructed objects and use the predicted structures to further guide 3D reconstruction. The unified framework is trained simultaneously offline to learn a generic notion and can be fine-tuned online for specific objects without any annotations. Extensive experiments on both synthetic and real data demonstrate that introducing 3D shape interpretation improves the performance of 3D reconstruction and vice versa, against the state-of-the-art algorithms on both seen and unseen categories."

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