Shape from Shading through Shape Evolution
This addresses shape reconstruction from images for computer vision applications, but it is incremental as it builds on existing deep learning and synthetic data methods.
The paper tackles the shape-from-shading problem by training deep networks with synthetic images generated through shape evolution, achieving state-of-the-art performance on a benchmark.
In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images. Our approach consists of two synergistic processes: the evolution of complex shapes from simple primitives, and the training of a deep network for shape-from-shading. The evolution generates better shapes guided by the network training, while the training improves by using the evolved shapes. We show that our approach achieves state-of-the-art performance on a shape-from-shading benchmark.