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3D Shape Classification

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

https://github.com/QingyongHu/SoTA-Point-Cloud

공식 코드

Deep Learning for 3D Point Clouds: A Survey

https://github.com/adonis-galaxy/dspoint

공식 코드

DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion

https://github.com/ajhamdi/MVTN

공식 코드

MVTN: Multi-View Transformation Network for 3D Shape Recognition

https://github.com/ajhamdi/mvtorch

공식 코드

MVTN: Learning Multi-View Transformations for 3D Understanding

https://github.com/auniquesun/vipformer

공식 코드

ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding

https://github.com/daniilidis-group/emvn

공식 코드

Equivariant Multi-View Networks

https://github.com/daniilidis-group/spherical-cnn

공식 코드

Learning Equivariant Representations

https://github.com/daniilidis-group/swscnn

공식 코드

Learning Equivariant Representations

https://github.com/donglaiw/ibexHelper

공식 코드

A Topological Nomenclature for 3D Shape Analysis in Connectomics

https://github.com/haotian-liu/maskpoint

공식 코드

Masked Discrimination for Self-Supervised Learning on Point Clouds

https://github.com/keeganhk/pointmcd

공식 코드

PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition

https://github.com/liuxinhai/FG3D-Net

공식 코드

Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions

https://github.com/meliao/rotation-invariant-random-features

공식 코드

Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds

https://github.com/mmmmimic/diffconvnet

공식 코드

diffConv: Analyzing Irregular Point Clouds with an Irregular View

https://github.com/myavartanoo/PolyNet_PyTorch

공식 코드

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

https://github.com/nenadmarkus/wlrn

공식 코드

Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval

https://github.com/prajwalsingh/TreeGCN-ED

공식 코드

TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network

https://github.com/xiangchong1/3d-adv-pc

공식 코드

Generating 3D Adversarial Point Clouds

https://github.com/yichen928/STRL

공식 코드

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds

https://github.com/zhh6425/LocalContextPropagation

공식 코드

LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers

https://github.com/LONG-9621/Generating-3D-Adversarial-Point-Clouds

Generating 3D Adversarial Point Clouds

https://github.com/TiagoCortinhal/SalsaNext

Deep Learning for 3D Point Clouds: A Survey

https://github.com/black0017/3D-GAN-pytorch

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

https://github.com/chinokenochkan/3dgan-keras

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

https://github.com/clintonjwang/di-net

Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation

https://github.com/code-implementation1/Code9/tree/main/PVNet

PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition

https://github.com/iMoonLab/MeshNet

MeshNet: Mesh Neural Network for 3D Shape Representation

https://github.com/machc/spherical_embeddings

Cross-Domain 3D Equivariant Image Embeddings

https://github.com/maxjiang93/DDSL

Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation

https://github.com/popcornell/keras-triplet-center-loss

Triplet-Center Loss for Multi-View 3D Object Retrieval

https://github.com/sudhir5595/Mesh_Neural_Network

MeshNet: Mesh Neural Network for 3D Shape Representation

https://github.com/vijaylaxmid/DeepLearning_3DPointClouds

Deep Learning for 3D Point Clouds: A Survey

https://github.com/wisconsinaivision/maskpoint

Masked Discrimination for Self-Supervised Learning on Point Clouds

https://github.com/xingyuansun/pix3d

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

https://github.com/xuxucmkox/3D-shape-Classification-Beam-Search

Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes

https://github.com/zck119/3dgan-release

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.