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Point Cloud Generation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gwxuan/reid3d
공식 코드https://github.com/hamedhaghighi/lidargrit
공식 코드Taming Transformers for Realistic Lidar Point Cloud Generation
https://github.com/haomengz/hyperpc
공식 코드https://github.com/hikvision-research/3dvision
공식 코드https://github.com/hongyuyann/symmcompletion
공식 코드SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidance
https://github.com/hrzhou2/seedformer
공식 코드SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer
https://github.com/hzxie/GRNet
공식 코드GRNet: Gridding Residual Network for Dense Point Cloud Completion
https://github.com/i2-multimedia-lab/proxyformer
공식 코드ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer
https://github.com/intellabs/mmpano
공식 코드L-MAGIC: Language Model Assisted Generation of Images with Coherence
https://github.com/ivansipiran/Refinement-Point-Cloud-Completion
공식 코드Refinement of Predicted Missing Parts Enhance Point Cloud Completion
https://github.com/j-geuter/ddeqs
공식 코드DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
https://github.com/jinec98/mae3d
공식 코드Masked Autoencoders in 3D Point Cloud Representation Learning
https://github.com/jinpeng-yu/geoformer
공식 코드GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated Transformer
https://github.com/johnromanelis/spvd
공식 코드Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
https://github.com/jw9730/setvae
공식 코드SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
https://github.com/kaiyizhang/AXform
공식 코드Attention-based Transformation from Latent Features to Point Clouds
https://github.com/kaiyizhang/SGAS
공식 코드Point Cloud Part Editing: Segmentation, Generation, Assembly, and Selection
https://github.com/kantamasuki/rgdm
공식 코드Generative diffusion model with inverse renormalization group flows
https://github.com/kazuto1011/dusty-gan
공식 코드https://github.com/kazuto1011/r2dm
공식 코드LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
https://github.com/klightz/PSF
공식 코드https://github.com/lfranke/vet
공식 코드VET: Visual Error Tomography for Point Cloud Completion and High-Quality Neural Rendering
https://github.com/ltwu6/cross-pcc
공식 코드Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion
https://github.com/ltwu6/malspc
공식 코드Self-supervised 3D Point Cloud Completion via Multi-view Adversarial Learning
https://github.com/luost26/diffusion-point-cloud
공식 코드Diffusion Probabilistic Models for 3D Point Cloud Generation
https://github.com/microsoft/SpareNet
공식 코드Style-based Point Generator with Adversarial Rendering for Point Cloud Completion
https://github.com/nini-lxz/Rotation-Invariant-Point-Cloud-Analysis
공식 코드A Rotation-Invariant Framework for Deep Point Cloud Analysis
https://github.com/nv-tlabs/LION
공식 코드https://github.com/ohhhyeahhh/pointattn
공식 코드PointAttN: You Only Need Attention for Point Cloud Completion
https://github.com/orrkrup/mace
공식 코드Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
https://github.com/parametric-completion/paco
공식 코드Parametric Point Cloud Completion for Polygonal Surface Reconstruction
https://github.com/paul007pl/MVP_Benchmark
공식 코드https://github.com/pclucas14/lidar_generation
공식 코드https://github.com/peterstratton/volume-droid
공식 코드Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM
https://github.com/pfilo8/flow-plugin-network-for-conditional-generation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.