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Found 5 papers, 3 papers with code
Elastic deformation of optical coherence tomography images of diabetic macular edema for deep-learning models training: how far to go?
To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME).
Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
In this paper, we propose a normal estimation method for unstructured 3D point clouds.
Ranked #8 on
Surface Normals Estimation
on PCPNet
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods.
Ranked #57 on
3D Part Segmentation
on ShapeNet-Part
3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder
We present an algorithm for registration between a large-scale point cloud and a close-proximity scanned point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems.
Graph Based Over-Segmentation Methods for 3D Point Clouds
This 3D information introduces a new conceptual change that can be utilized to improve the results of over-segmentation, which uses mainly color information, and to generate clusters of points we call super-points.
