Despite strong progress in the field of 3D reconstruction from multiple views, holes on objects, transparency of objects and textureless scenes, continue to be open challenges. On the other hand, silhouette based reconstruction techniques ease the dependency of 3d reconstruction on image pixels but need a large number of silhouettes to be available from multiple views. In this paper, a novel end to end pipeline is proposed to produce high quality reconstruction from a low number of silhouettes, the core of which is a deep shape reconstruction architecture. Evaluations on ShapeNet [1] show good quality of reconstruction compared with ground truth.
In this paper, we propose an end-to-end model for producing furniture layout for interior scene synthesis from a random vector. This proposed model is aimed to support professional interior designers to produce interior decoration solutions more quickly. The proposed model combines a conditional floor-plan module of the room, a conditional graphical floor-plan module of the room, and a conditional layout module. Compared with the prior work on scene synthesis, our proposed three modules enhance the ability of auto-layout generation given the dimensional category of the room. We conduct our experiments on a proposed real-world interior layout dataset that contains 191, 208 designs from the professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts in comparison with the state-of-art model. The dataset and codes are available at https: //github.com/CODE-SUBMIT/dataset3.
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