Shypd.aiThe largest open index of AI toolsPoint-BERT is an open-source AI research tool that pre-trains 3D point cloud Transformers using masked point modeling. It enables advanced research in 3D data classification and segmentation.
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Point-BERT is a PyTorch implementation of a novel pre-training paradigm for 3D point cloud Transformers, introduced in CVPR 2022. Inspired by BERT, it utilizes a Masked Point Modeling (MPM) task where point clouds are divided into local patches, and a discrete Variational AutoEncoder (dVAE) tokenizes these patches. The pre-training objective involves recovering original point tokens at masked locations, supervised by the dVAE's output. This method significantly advances the capabilities of Transformers for 3D data, facilitating tasks like classification on ModelNet40 and ScanObjectNN, few-shot learning, and part segmentation on ShapeNetPart. It is an essential tool for researchers and engineers working with 3D point cloud analysis.
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