: Given a single image, our algorithm estimates the surface normal at each pixel. Notice how our algorithm not only estimates the coarse structure but also captures fine local details. For example, on the left, the normals of the couch arm and side table legs are estimated accurately (see zoomed version). On the right, the chair surface and legs and even the top of the shopping bags are captured correctly. Normal legend: blue → X; green → Y; red → Z. In the past few years, convolutional neural nets (CNN) have shown incredible promise for learning visual representations. In this paper, we use CNNs for the task of predicting surface normals from a single image. But what is the right architecture? We propose to build upon the decades of hard work in 3D scene understanding to design a new CNN architecture for the task of surface normal estimation. We show that incorporating several constraints (man-made, Manhattan world) and meaningful intermediate representations (room layout, edge labels) in the architecture leads to state of the art performance on surface normal estimation. We also show that our network is quite robust and show state of the art results on other datasets as well without any fine-tuning.
In REF , Wang et al.explore the effectiveness of CNNs for the tasks of surface normal estimation.
Designing deep networks for surface normal estimation
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science