https://github.com/tejaskhot/unsup_mvs
Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency
Papers with Code 아카이브
3D geometry
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ttaa9/genren
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
https://github.com/vkvats/GC-MVSNet
Blending 3D Geometry and Machine Learning for Multi-View Stereopsis
https://github.com/vnoves/aectech2019-sketchto3d-backend
Hierarchical Surface Prediction for 3D Object Reconstruction
https://github.com/xhxuciedu/3DHand
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
https://github.com/xiuyu0000/new_papers_codes/tree/main/cmr
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/xxlong0/wonder3d
https://github.com/yangyucheng000/NeuralRecon
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
https://github.com/yilundu/cross_attention_renderer
Learning to Render Novel Views from Wide-Baseline Stereo Pairs
https://github.com/yixunliang/unitex
CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets
https://github.com/yxuhan/adampi
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images
https://github.com/zhengtr/CS231N-Final-Project
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/zhenheny/LEGO
LEGO: Learning Edge with Geometry all at Once by Watching Videos
https://github.com/ziyuguo99/viewrefer3d
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
https://github.com/zxpzhong/3D-RecGAN-pytorch
3D Object Reconstruction from a Single Depth View with Adversarial Learning
https://github.com/zzxmllq/cnn_com
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.