Hierarchical Surface Prediction for 3D Object Reconstruction
Christian Häne,Shubham Tulsiani,Jitendra Malik +2 more
- pp 412-420
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TLDR
This work proposes a general framework, called hierarchical surface prediction (HSP), which facilitates prediction of high resolution voxel grids, and shows that high resolution predictions are more accurate than low resolution predictions.Abstract:
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.read more
Citations
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Proceedings ArticleDOI
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
TL;DR: DeepSDF as mentioned in this paper represents a shape's surface by a continuous volumetric field: the magnitude of a point in the field represents the distance to the surface boundary and the sign indicates whether the region is inside (-) or outside (+) of the shape.
Proceedings ArticleDOI
Learning Implicit Fields for Generative Shape Modeling
Zhiqin Chen,Hao Zhang +1 more
TL;DR: In this paper, an implicit field is used to assign a value to each point in 3D space, so that a shape can be extracted as an iso-surface, and a binary classifier is trained to perform this assignment.
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Occupancy Networks: Learning 3D Reconstruction in Function Space
TL;DR: This paper proposes Occupancy Networks, a new representation for learning-based 3D reconstruction methods that encodes a description of the 3D output at infinite resolution without excessive memory footprint, and validate that the representation can efficiently encode 3D structure and can be inferred from various kinds of input.
Proceedings ArticleDOI
Occupancy Networks: Learning 3D Reconstruction in Function Space
TL;DR: In this paper, the authors propose Occupancy Networks, which implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier, which can be used for learning-based 3D reconstruction methods.
Proceedings ArticleDOI
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
TL;DR: Pixel-aligned Implicit Function (PIFu) as mentioned in this paper aligns pixels of 2D images with the global context of their corresponding 3D object to produce highresolution surfaces including largely unseen regions such as the back of a person.
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