Papers with Code 아카이브

3D Classification

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코드

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

https://github.com/yossilevii100/refocusing

공식 코드

Robustifying Point Cloud Networks by Refocusing

https://github.com/zhimin-c/multiview-mae

공식 코드

Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder

https://github.com/ziyuguo99/point-bind_point-llm

공식 코드

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

https://gitlab.oit.duke.edu/cvit-public/cvit_revicovid19

공식 코드

Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging

https://github.com/AliBahri94/SVWA_TTA

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

https://github.com/CPUFronz/CapsVoxGAN

3D Point Capsule Networks

https://github.com/Colin97/OpenShape_code

OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

https://github.com/CorleoneJW/3D-Retrieval-System

A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification

https://github.com/Daniel-Liu-c0deb0t/3D-Neural-Network-Adversarial-Attacks

Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

https://github.com/Pointcept/GPT4Point

3D-LLM: Injecting the 3D World into Large Language Models

https://github.com/WDot/G3DNet

https://github.com/Yui010206/CREMA

3D-LLM: Injecting the 3D World into Large Language Models

https://github.com/aidinhass/tf-sphcnn

Learning SO(3) Equivariant Representations with Spherical CNNs

https://github.com/baaivision/GeoDream

Uni3D: Exploring Unified 3D Representation at Scale

https://github.com/boyden/pointtransformerfl

PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

https://github.com/code-implementation1/Code1/tree/main/DDAG

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

https://github.com/google-research/spherical-cnn

Learning SO(3) Equivariant Representations with Spherical CNNs

https://github.com/guerbet-ai/wsp-contrastive

Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification

https://github.com/hamidreza-dastmalchi/3dd-tta

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

https://github.com/hrisi/tree-species-classification

PointMixer: MLP-Mixer for Point Cloud Understanding

https://github.com/ismav/sparseNMF

Block Coordinate Descent for Sparse NMF

https://github.com/kondratevakate/3DDeformableConvolutions

3D Deformable Convolutions for MRI classification

https://github.com/kondratevakate/3d-deformable-convolutions

3D Deformable Convolutions for MRI classification

https://github.com/krips89/mlh_mvcnn

Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks

https://github.com/linhaojia13/pointmetabase

PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

https://github.com/mediteamC/teamC

Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification

https://github.com/minzhang-1/PointHop-PointHop2_Spark

PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification

https://github.com/minzhang-1/PointHop2

PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification

https://github.com/mleming/general_class_balancer

Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks

https://github.com/neuro-ml/resnet_cnn_mri_adni

Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification

https://github.com/pradneshbhike/CNN-for-AD-diagnosis

Enhancing Learnability of classification algorithms using simple data preprocessing in fMRI scans of Alzheimer's disease

https://github.com/qizekun/ReCon

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

https://github.com/qq456cvb/Point-Transformers

Point Transformer

https://github.com/runpeidong/act

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

https://github.com/rusty1s/pytorch_cluster

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

https://github.com/snehaputul/CrossMoCo

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