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ModelNet10

3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs.

Data and Resources

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Cite this as

Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro (2024). Dataset: ModelNet10. https://doi.org/10.57702/q6jhri4k

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Additional Info

Field	Value
Created	December 2, 2024
Last update	December 2, 2024
Defined In	https://doi.org/10.48550/arXiv.2210.00376
Citation	https://doi.org/10.48550/arXiv.2002.03281 https://doi.org/10.48550/arXiv.1711.08241 https://doi.org/10.48550/arXiv.2306.04701 https://doi.org/10.48550/arXiv.1804.04458
Author	Zhiyang Wang
More Authors	Luana Ruiz Alejandro Ribeiro
Homepage	https://cs.nyu.edu/~ylclab/data/ShapeNetCoreV1.zip