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3D Classification

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hkust-vgd/scanobjectnn

공식 코드

Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

https://github.com/hlei-ziyan/SPH3D-GCN

공식 코드

Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds

https://github.com/iducvlab/cov19d

공식 코드

Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images

https://github.com/jiamang/ibt

공식 코드

Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

https://github.com/jmiemirza/mate

공식 코드

MATE: Masked Autoencoders are Online 3D Test-Time Learners

https://github.com/kanezaki/rotationnet

공식 코드

RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

https://github.com/lifebeyondexpectations/eccv22-pointmixer

공식 코드

PointMixer: MLP-Mixer for Point Cloud Understanding

https://github.com/lorenmt/vsl

공식 코드

Learning a Hierarchical Latent-Variable Model of 3D Shapes

https://github.com/lyqun/FPConv

공식 코드

FPConv: Learning Local Flattening for Point Convolution

https://github.com/lzhengning/SubdivNet

공식 코드

Subdivision-Based Mesh Convolution Networks

https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification

공식 코드

A Graph-CNN for 3D Point Cloud Classification

https://github.com/mcg-nju/app-net

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APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification

https://github.com/mcr-peft/ex-mcr

공식 코드

Extending Multi-modal Contrastive Representations

https://github.com/megayeye/triangle-net

공식 코드

Triangle-Net: Towards Robustness in Point Cloud Learning

https://github.com/mmmmimic/diffconvnet

공식 코드

diffConv: Analyzing Irregular Point Clouds with an Irregular View

https://github.com/mohamedafham/crosspoint

공식 코드

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding

https://github.com/mr-neko/jm3d

공식 코드

Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation

https://github.com/mutianxu/GDANet

공식 코드

Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud

https://github.com/myavartanoo/PolyNet_PyTorch

공식 코드

PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation

https://github.com/mys007/ecc

공식 코드

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

https://github.com/nexer8/se3d

공식 코드

SE3D: A Framework For Saliency Method Evaluation In 3D Imaging

https://github.com/openrobotlab/pointllm

공식 코드

PointLLM: Empowering Large Language Models to Understand Point Clouds

https://github.com/qizekun/ShapeLLM

공식 코드

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

https://github.com/ranahanocka/MeshCNN

공식 코드

MeshCNN: A Network with an Edge

https://github.com/raoyongming/PointGLR

공식 코드

Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds

https://github.com/saba99/classifying-objects-3d-point-clouds

공식 코드

Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach

https://github.com/salesforce/ulip

공식 코드

ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding

https://github.com/sgi-2023/3d-building-classification

공식 코드

DC3DO: Diffusion Classifier for 3D Objects

https://github.com/snehaputul/allmatch

공식 코드

Improving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data

https://github.com/tangyuan96/minigpt-3d

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MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors

https://github.com/tsbiosky/PointACL

공식 코드

PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack

https://github.com/weiguangzhao/diff-op3d

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Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges

https://github.com/xiaogangw/cascaded-point-completion

공식 코드

Cascaded Refinement Network for Point Cloud Completion with Self-supervision

https://github.com/xuechaochen/p3p-mae

공식 코드

P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders

https://github.com/yangyangyang127/pointclip_v2

공식 코드

PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning

https://github.com/yossilevii100/critical_points2

공식 코드

Robustifying Point Cloud Networks by Refocusing

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