Papers with Code 아카이브
Point Cloud Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zhanhz/krf
공식 코드PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation
https://github.com/zhaoyanglyu/point_diffusion_refinement
공식 코드A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
https://github.com/zray26/dr-pose
공식 코드https://github.com/zyyyyy0926/pointcformer_plus_pytorch
공식 코드PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion
https://github.com/1zb/3dshape2vecset
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/ktnet
https://github.com/2023-MindSpore-4/Code11/tree/main/SPC-Net
https://github.com/2023-MindSpore-4/Code14/tree/main/SPC-Net
https://github.com/2023-MindSpore-4/Code7/tree/main/SPC-Net
https://github.com/ANLGBOY/SoftFlow
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
https://github.com/AnTao97/FoldingNet.pytorch
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
https://github.com/AnTao97/UnsupervisedPointCloudReconstruction
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
https://github.com/ChenXuelinCXL/pcl2pcl-gan-pub
Unpaired Point Cloud Completion on Real Scans using Adversarial Training
https://github.com/DiT-3D/DiT-3D
DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation
https://github.com/DreamBlack/APCNet
https://github.com/EricDai0/AdvGCGAN
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
https://github.com/Guillem96/pointer-nn-pytorch
https://github.com/Howardhuang98/Pointer_network
https://github.com/JohnRomanelis/SPVD_Lightning
Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
https://github.com/JoseVillagranE/Pointer-Networks
https://github.com/KolodziejczykWaldemar/universal-ptr-network
https://github.com/LukeBolly/tf-tspn
https://github.com/MChaus/NeoRender_test_task
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
https://github.com/MS-Mind/MS-Code-04
https://github.com/MStypulkowski/CIF
https://github.com/MStypulkowski/mlinpl-generative-workshop
https://github.com/MaciejZamorski/3d-AAE
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
https://github.com/MindSpore-paper-code-3/code9/tree/main/ktnet
https://github.com/NTT123/pointer-networks
https://github.com/Nina-Konovalova/TSP-RL-Skoltech_project
https://github.com/Rintarooo/TSP_DRL_PointerNet
https://github.com/Rintarooo/TSP_DRL_PtrNet
https://github.com/Rintarooo/TSP_RL_Pointer_Actor_Critic
https://github.com/Seed3D/Dora
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
https://github.com/ahmadelsallab/lidargan
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.