[Paper]3D Vision Papers
Shape representation
Depth Maps
(NIPS 14’) Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
- Loss : Scale Invariant error
$D(y,y^*)={1\over n} \Sigma_i d_i^2 - {1\over n^2} \Sigma_{i,j} d_id_j$
- Archtiecture: for Multi scale Image [Image]
(ICCV 15’) Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
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Problem Definition Input : 2D Image
Output : Depth Predction, Surface Normal Estimation, Labels(for every pixel) -
Idea 3 Task at once: Depth Prediction, Surface Normal Estimation, Semantic Segmentation
Generate directly from input image -
Limitation
Every task requires a supervised learning. Therefore ground truth value is needed!
- Loss
Of course, 3 different tasks have task specific loss!
Depth : Scale Invariant Loss Again!
Surface Normal :
$L_{normals}(N,N^) = - {1\over n} \sum_i N_i\cdot N_i^ = -{1\over n} N\cdot N^$
$N$ : predicted normal vector map
$N^$: ground true normal vector map
Calculate normal vector component at (x,y,z): from 1 channel into 3 channel!
Semantic Lables
$L_{semantic}(C,C^) = -{1\over n} \sum_i C_i^\log (C_i)$
Pixelwise softmax classifiers!
- train
Supervised learning! Every task use supervised learning
- Architecture : Multi-scale
[Archtecture]
How to deliver gradiient(updates) for every scale-specific network?
Key is that how to train whole network. For now, it is natural to use an input as the previous layer output.
Voxel Grid
(CVPR 15’) 3D ShapeNets: A Deep Representation for Volumetric Shapes
- Problem Definition
Input : 2D Image + Depth value
Output: Shape Completion
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Idea Train generic shape representation
Geometric 3D shape as a ** probabilistic distribution of binary variable** on a 3D voxel grid -
Limitation
Binary representation of 3D is large and sparse. Scalability might be problematic.
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Loss
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Train
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Downstream task
Because it is a probabilistic model, there are somewhat different inference process!
- Architecture: Convolution for reducing parameters
Point Cloud
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Problem Definition
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Idea
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Limitation
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Loss
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train
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Architecture
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