DASI: Learning Domain Adaptive Shape Impression for 3D Object Reconstruction

Previous 3D object reconstruction methods from 2D images involve two issues: the lack of in-depth exploration of the prior knowledge of 3D shapes, and the difficulty of dealing with the serious occluded parts. Inspired by human’s perception on real-world objects which is composed of an overall impression (known as
shape impression
) and an enhanced cognition, we propose a deep network (denoted by DASI) to learn the Domain Adaptive Shape Impression for 3D reconstruction from arbitrary view images. DASI consists of two modules: shape reconstruction module and shape refinement module. The former module reconstructs a coarse volume by learning a domain adaptive shape impression as embedding in image-based reconstruction. We first leverage 3D objects to learn a shape impression being associated with prior knowledge of 3D objects. To attain consensus on shape impression from 2D images, we regard the 3D shape and the 2D image as two different domains. By adapting the two domains, the shape impression learned from 3D objects is transferred to 2D images and guides the images-based reconstruction. The latter module refines the objects by modeling the whole 3D volume to local 3D patches and exploring their intrinsic geometry relationships. Quantitative and qualitative experimental results on two benchmark datasets demonstrate that DASI outperforms several state-of-the-arts for 3D reconstruction from single and multi-view 2D images.