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Found 5 papers, 4 papers with code
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View
Existing datasets on groceries are mainly 2D images.
Local Neighborhood Features for 3D Classification
We train and evaluate PointNeXt on ModelNet40 (synthetic), ScanObjectNN (real-world), and a recent large-scale, real-world grocery dataset, i. e., 3DGrocery100.
Ranked #21 on
3D Point Cloud Classification
on ScanObjectNN
SimpleView++: Neighborhood Views for Point Cloud Classification
Among these methods, the Simple View model demonstrates that features from six orthogonal perspective projections of a point cloud achieved comparable 3D classification.
Ranked #42 on
3D Point Cloud Classification
on ScanObjectNN
PatchAugment: Local Neighborhood Augmentation in Point Cloud Classification
Methods using 3D datasets are among the most common to use data augmentation techniques such as random point drop, scaling, translation, rotations, and jittering.
Ranked #54 on
3D Point Cloud Classification
on ScanObjectNN
Dynamic Local Geometry Capture in 3D PointCloud Classification
We propose a novel technique of dynamically oriented and scaled ellipsoid based on unique local information to capture the local geometry better.
Ranked #53 on
3D Point Cloud Classification
on ScanObjectNN
