1,586 research outputs found
Hierarchical Surface Prediction for 3D Object Reconstruction
Recently, Convolutional Neural Networks have shown promising results for 3D
geometry prediction. They can make predictions from very little input data such
as a single color image. A major limitation of such approaches is that they
only predict a coarse resolution voxel grid, which does not capture the surface
of the objects well. We propose a general framework, called hierarchical
surface prediction (HSP), which facilitates prediction of high resolution voxel
grids. The main insight is that it is sufficient to predict high resolution
voxels around the predicted surfaces. The exterior and interior of the objects
can be represented with coarse resolution voxels. Our approach is not dependent
on a specific input type. We show results for geometry prediction from color
images, depth images and shape completion from partial voxel grids. Our
analysis shows that our high resolution predictions are more accurate than low
resolution predictions.Comment: 3DV 201
A survey on deep geometry learning: from a representation perspective
Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth images, multi-view images, voxels, point clouds, meshes, implicit surfaces, etc. The performance achieved in different applications largely depends on the representation used, and there is no unique representation that works well for all applications. Therefore, in this survey, we review recent developments in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations for different applications. We also present existing datasets in these representations and further discuss future research directions
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