Papers with Code 아카이브
Single-View 3D Reconstruction
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AiuniAI/Unique3D
공식 코드Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
https://github.com/AnnnnnieZhang/M3D
공식 코드https://github.com/KU-CVLAB/3DFuse
공식 코드Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
https://github.com/NikolaZubic/2dimageto3dmodel
공식 코드An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering
https://github.com/ShichenLiu/SoftRas
공식 코드Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
https://github.com/ShivamDuggal4/TARS3D
공식 코드Topologically-Aware Deformation Fields for Single-View 3D Reconstruction
https://github.com/SkyworkAI/Gamba
공식 코드Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction
https://github.com/VITA-Group/NeuralLift-360
공식 코드NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views
https://github.com/Xharlie/DISN
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/Xharlie/ShapenetRender_more_variation
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/YueJiang-nj/CVPR2020-SDFDiff
공식 코드SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
https://github.com/aim-uofa/AdelaiDepth
공식 코드https://github.com/chenhsing/ssp3d
공식 코드Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors
https://github.com/cv-rits/MonoScene
공식 코드https://github.com/cvlab-columbia/zero123
공식 코드https://github.com/czq142857/implicit-decoder
공식 코드https://github.com/facebookresearch/mcc
공식 코드https://github.com/garyzhu1996/umiformer
공식 코드UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction
https://github.com/hyperparams-free/hyperparams-free-3D-losses
공식 코드Hyperparameter-Free Losses for Model-Based Monocular Reconstruction
https://github.com/kuangzijian/drifu-for-animals
공식 코드https://github.com/laughtervv/DISN
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/layumi/3D-Magic-Mirror
공식 코드3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective
https://github.com/liuyuan-pal/syncdreamer
공식 코드SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
https://github.com/monniert/unicorn
공식 코드Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
https://github.com/muelea/buddi
공식 코드Generative Proxemics: A Prior for 3D Social Interaction from Images
https://github.com/nicolaihaeni/corn
공식 코드Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision
https://github.com/nitinagarwal/GAMesh
공식 코드GAMesh: Guided and Augmented Meshing for Deep Point Networks
https://github.com/nwuzhouwei/ssmp
공식 코드https://github.com/robotic-vision-lab/learning-implicitly-from-spatial-transformers-network
공식 코드LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstruction
https://github.com/sail-sg/numcc
공식 코드NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
https://github.com/sascha-kirch/VoloGAN
공식 코드VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data
https://github.com/szymanowiczs/splatter-image
공식 코드https://github.com/val-iisc/3d-lmnet
공식 코드https://github.com/xrenaa/CS-DisMo
공식 코드Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement
https://github.com/yefanzhou/dispersion-score
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.