3D object recognition is the task of recognising objects from 3D data.
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We study the problem of 3D object generation.
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
SOTA for 3D Object Recognition on ModelNet40
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
#3 best model for 3D Object Recognition on ModelNet40
The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object.
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.