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Found 5 papers, 3 papers with code
A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks
Our finding shows that a leading factor in determining recognition versus reconstruction is how dispersed the training data is.
OpenEDS: Open Eye Dataset
We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination.
The Impact of Quantity of Training Data on Recognition of Eating Gestures
Experiments are described that explore the complexity of hidden Markov models (HMMs) and the amount of training data needed to adequately capture the motion variability across this large data set.
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure.
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Recent deep networks that directly handle points in a point set, e. g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation.
Ranked #16 on
3D Point Cloud Linear Classification
on ModelNet40
