https://github.com/val-iisc/capnet
공식 코드CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
Papers with Code 아카이브
Shape Representation Of 3D Point Clouds
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/val-iisc/densepcr
공식 코드Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
https://github.com/val-iisc/ssl_3d_recon
공식 코드From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks
https://github.com/vvrpanda/exppoint-mae
공식 코드ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers
https://github.com/wangzy22/P2P
공식 코드P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
https://github.com/wangzy22/tap
공식 코드Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
https://github.com/weiguangzhao/diff-op3d
공식 코드https://github.com/xhanxu/Mamba3D
공식 코드Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
https://github.com/xiaogangw/cascaded-point-completion
공식 코드Cascaded Refinement Network for Point Cloud Completion with Self-supervision
https://github.com/xiwuchen/pra-net
공식 코드PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis
https://github.com/yahuiliu99/pointcont
공식 코드Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space
https://github.com/yangfengseu/cedr
공식 코드https://github.com/yangyangyang127/pointclip_v2
공식 코드PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
https://github.com/yangyanli/PointCNN
공식 코드https://github.com/yangyeeee/apsnet
공식 코드https://github.com/yanx27/PointASNL
공식 코드PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
https://github.com/yhw-yhw/PVAMVSNet
공식 코드Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
https://github.com/yichen928/STRL
공식 코드Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
https://github.com/yunlu-chen/PointMixup
공식 코드https://github.com/zhang-tao-whu/pcm
공식 코드Point Cloud Mamba: Point Cloud Learning via State Space Model
https://github.com/zhanheshen/pointcmt
공식 코드Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
https://github.com/zhh6425/LocalContextPropagation
공식 코드LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers
https://github.com/zhimin-c/multiview-mae
공식 코드Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder
https://github.com/ziyuguo99/point-bind_point-llm
공식 코드https://github.com/zrrskywalker/i2p-mae
공식 코드Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
https://github.com/zrrskywalker/point-m2ae
공식 코드Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
https://github.com/zrrskywalker/pointclip
공식 코드https://github.com/zsc000722/ppt
공식 코드Positional Prompt Tuning for Efficient 3D Representation Learning
https://github.com/zyh16143998882/IDPT
공식 코드Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
https://github.com/zyh16143998882/aaai24-pointfemae
공식 코드Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
https://github.com/zyh16143998882/iccv23-idpt
공식 코드Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
https://gitee.com/gai-shaoyan/mind3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/04RR/SOTA-Vision
https://github.com/1wy/bert
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Transformer
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointtransformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.