https://github.com/ActiveVisionLab/porf
공식 코드PoRF: Pose Residual Field for Accurate Neural Surface Reconstruction
Papers with Code 아카이브
Multi-View 3D Reconstruction
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AlexandreBinninger/TetWeave
공식 코드https://github.com/AndreeaDogaru/SphereGuided
공식 코드https://github.com/CLinvx/HPM-MVS
공식 코드https://github.com/CyberAgentAILab/SuperNormal
공식 코드SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration
https://github.com/EnVision-Research/Ref-NeuS
공식 코드https://github.com/GaryZhu1996/GARNet
공식 코드GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff
https://github.com/GhiXu/ACMP
공식 코드https://github.com/Yang7879/AttSets
공식 코드Robust Attentional Aggregation of Deep Feature Sets for Multi-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/autonomousvision/differentiable_volumetric_rendering
공식 코드Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
https://github.com/bbrument/rnb-neus
공식 코드RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction
https://github.com/ckh0715/DPE-MVS
공식 코드Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization
https://github.com/facebookresearch/efm3d
공식 코드EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models
https://github.com/facebookresearch/fast3r
공식 코드Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
https://github.com/faridyagubbayli/LegoFormer
공식 코드LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction
https://github.com/fdarmon/neuralwarp
공식 코드Improving neural implicit surfaces geometry with patch warping
https://github.com/fomalhautb/3d-retr
공식 코드3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with Transformers
https://github.com/fraunhoferhhi/neural-deferred-shading
공식 코드https://github.com/hzxie/Pix2Vox
공식 코드Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
https://github.com/liyingcv/long-range-grouping-transformer
공식 코드Long-Range Grouping Transformer for Multi-View 3D Reconstruction
https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch
공식 코드Extremely Dense Point Correspondences using a Learned Feature Descriptor
https://github.com/qianyiwu/objectsdf_plus
공식 코드ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces
https://github.com/tiongleslie/3D-C2FT
공식 코드3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction
https://github.com/vkvats/GC-MVSNet
공식 코드GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo
https://github.com/za-cheng/PM-PMVS
공식 코드Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance
https://github.com/zhenpeiyang/hm3d-abo
공식 코드HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction
https://github.com/zvict/papr
공식 코드https://github.com/Ajithbalakrishnan/3D-Model-Reconstruction
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
https://github.com/Ajithbalakrishnan/3D-Object-Reconstruction-from-Multi-View-Monocular-RGB-images
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
https://github.com/CrisalixSA/h3ds
https://github.com/Radhika009/CMPE_295B_MASTERPROJECT
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
https://github.com/Skoltech-3D/sk3d_data
Multi-sensor large-scale dataset for multi-view 3D reconstruction
https://github.com/autonomousvision/monosdf
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
https://github.com/envision-research/lotus
Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
https://github.com/whoiszzj/apd-mvs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.