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.

(latest updates and bug fixed) DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction

Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features

Implementation of "Long-Range Grouping Transformer for Multi-View 3D Reconstruction" [ICCV 2023]

This is the official repository of the original Point Transformer architecture.

Learning Local Displacements for Point Cloud Completion - CVPR 2022

PointNet implementation on Windows (pyTorch version, most of codes are brorrowed from Fxia22)

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.

A lightweight neural network for multi-view classification and reconstruction.

Three-dimensional model retrieval in single category.

PointNet & ShapeNet

Python implementation of LDGCNN with attention layers

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