A graph total variation regularizer on surface normals, optimized with ADMM on a bipartite graph, upsamples 3D point clouds with lower point-to-point and point-to-plane errors than APSS and RIMLS.
SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
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abstract
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.
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2019 1verdicts
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3D Point Cloud Super-Resolution via Graph Total Variation on Surface Normals
A graph total variation regularizer on surface normals, optimized with ADMM on a bipartite graph, upsamples 3D point clouds with lower point-to-point and point-to-plane errors than APSS and RIMLS.