SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
This addresses 3D reconstruction from multiple views, offering a novel end-to-end learning approach for computer vision tasks.
The paper tackles multiview stereopsis by proposing SurfaceNet, an end-to-end 3D neural network that directly infers 3D models from images and camera parameters, achieving results evaluated on the DTU benchmark.
This paper proposes an end-to-end learning framework for multiview stereopsis. We term the network SurfaceNet. It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model. The key advantage of the framework is that both photo-consistency as well geometric relations of the surface structure can be directly learned for the purpose of multiview stereopsis in an end-to-end fashion. SurfaceNet is a fully 3D convolutional network which is achieved by encoding the camera parameters together with the images in a 3D voxel representation. We evaluate SurfaceNet on the large-scale DTU benchmark.