Single Image Shape-from-Silhouettes
Yawen Lu, Yuxing Wang, Guoyu Lu · 2020
Recovering a 3D shape representation from one single image input has been attempted in recent years. Most of the works obtain 3D models from multiple images at different perspectives or ground truth CAD models. However, multiple images from different perspectives or 3D CAD models are not always available in real applications. In this work, we present a novel shape-from-silhouette method based on just a single image, which is an end-to-end learning framework relying on view synthesis and shape-from-silhouette methodology to reconstruct a 3D shape. The reconstructed 3D mesh can approach the real shape of target objects by constraining the silhouettes from both horizontal and vertical directions, especially for those objects with occlusions. Our proposed method achieves state-of-the-art performance on the ShapeNet dataset compared with other recent approaches targeting 3D reconstruction from a single image. Without requiring labor-intensive and time-consuming human annotations, the work has a broad potential to be applied in real-world applications.