https://github.com/vishal3477/Reverse_Engineering_GMs
공식 코드Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.
Image: Gwak et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vision3d-lab/ditto
공식 코드DITTO: Dual and Integrated Latent Topologies for Implicit 3D Reconstruction
https://github.com/vkvats/GC-MVSNet
공식 코드GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo
https://github.com/vladhondru25/exddv
공식 코드ExDDV: A New Dataset for Explainable Deepfake Detection in Video
https://github.com/vpsg-research/waveguard
공식 코드https://github.com/vuenc/slice-to-shape
공식 코드On the Localization of Ultrasound Image Slices within Point Distribution Models
https://github.com/wagnew3/ARM
공식 코드Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
https://github.com/wangjk666/M2TR-Multi-modal-Multi-scale-Transformers-for-Deepfake-Detection
공식 코드M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection
https://github.com/wangkua1/nemo-cvpr2023
공식 코드NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action
https://github.com/wangty1/DCPT
공식 코드Deep Convolutional Pooling Transformer for Deepfake Detection
https://github.com/wangty1/LampMark
공식 코드LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks
https://github.com/waps101/MatlabRenderer
공식 코드What does 2D geometric information really tell us about 3D face shape?
https://github.com/warplab/megafishdetector
공식 코드https://github.com/wasim004/FAME
공식 코드FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes
https://github.com/waterljwant/SSC
공식 코드Anisotropic Convolutional Networks for 3D Semantic Scene Completion
https://github.com/wayne-mai/egoloc
공식 코드EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queries
https://github.com/weders/RoutedFusion
공식 코드https://github.com/wenbin-lin/OcclusionFusion
공식 코드OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction
https://github.com/wenjiawang0312/zolly
공식 코드Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction
https://github.com/whu-usi3dv/deepaat
공식 코드DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mapping
https://github.com/whu-usi3dv/whu-synthetic
공식 코드WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research
https://github.com/whubaichuan/M3VSNet
공식 코드M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
https://github.com/willemeng/VLScene
공식 코드VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completion
https://github.com/windrise/3dgr-car
공식 코드https://github.com/wllmzhu/G-VUE
공식 코드Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation
https://github.com/wolo-wolo/FFR_FD-Effective-and-Fast-Detection-of-DeepFakes-Based-on-Feature-Point-Defects
공식 코드FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects
https://github.com/wolo-wolo/FSFM
공식 코드https://github.com/wrk226/2d-to-3d-evolution-transfer
공식 코드Creative Birds: Self-Supervised Single-View 3D Style Transfer
https://github.com/wrk226/creative_birds
공식 코드Creative Birds: Self-Supervised Single-View 3D Style Transfer
https://github.com/wrld/free-surgs
공식 코드Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene Reconstruction
https://github.com/wscstrive/ct-mvsnet
공식 코드CT-MVSNet: Efficient Multi-View Stereo with Cross-scale Transformer
https://github.com/wuuu3511/gomvs
공식 코드GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo
https://github.com/wyddmw/pomato
공식 코드POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction
https://github.com/wzzheng/tpvformer
공식 코드Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction
https://github.com/xianyunsun/vra
공식 코드https://github.com/xiaohao-xu/mac-ego3d
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.