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We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features
This is the official repository of the original Point Transformer architecture.
Implementation of "Long-Range Grouping Transformer for Multi-View 3D Reconstruction" [ICCV 2023]
A python script to convert and down-sample mesh data into pointclouds using FPS algorithm.
The code repository of DGCNN on FPGA: Acceleration of The Point Cloud Classifier Using FPGAs
3D-shape compression with a shape-coded auto-decoder and sinusoidal activations.
Three-dimensional model retrieval in single category.
PointNet & ShapeNet
Explore the impact of occlusion on Pix2Vox, a multi-view 3D reconstruction model, and possible approaches for improving its generated 3D shapes, including inpainting using StableDiffusion.
Point Cloud Segmentation Using PointNet