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Found 8 papers, 5 papers with code
Regularized Discrete Optimal Transport
The resulting transportation plan can be used as a color transfer map, which is robust to mass variation across images color palettes.
Normal Integration: A Survey
In the first part of this survey, we select the most important properties that one may expect from a normal integration method, based on a thorough study of two pioneering works by Horn and Brooks [28] and by Frankot and Chellappa [19].
Variational Methods for Normal Integration
The need for an efficient method of integration of a dense normal field is inspired by several computer vision tasks, such as shape-from-shading, photometric stereo, deflectometry, etc.
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud.
LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net
We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud.
Projected gradient descent for non-convex sparse spike estimation
We propose a new algorithm for sparse spike estimation from Fourier measurements.
Fast off-the-grid sparse recovery with over-parametrized projected gradient descent
We consider the problem of recovering off-the-grid spikes from Fourier measurements.
Patch-based image Super Resolution using generalized Gaussian mixture model
The minimum mean square error (MMSE) methodis a powerful image restoration method that uses a probability model on the patches of images.
