https://github.com/aakash4305/gbdf
공식 코드GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 10
- 보존된 평가 기록
- 44
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/aaltoml/nsvqgs
공식 코드Compressing 3D Gaussian Splatting by Noise-Substituted Vector Quantization
https://github.com/aaltoml/uncertainty-nerf-gs
공식 코드https://github.com/aaroncomo/lipfd
공식 코드https://github.com/abhi1kumar/SUP-NeRF
공식 코드https://github.com/abhinavdhall/deepfake
공식 코드Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
https://github.com/abrilcf/3d-3d_repeat-concatenate
공식 코드Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling
https://github.com/acasamitjana/3dhirest
공식 코드https://github.com/acis2021/viewplanningtoolbox
공식 코드Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction
https://github.com/adipiz99/RawNetLite
공식 코드https://github.com/adonis-galaxy/wordepth
공식 코드WorDepth: Variational Language Prior for Monocular Depth Estimation
https://github.com/adrianastan/tada
공식 코드TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
https://github.com/ah651/deepfake_adv
공식 코드https://github.com/ahaliassos/RealForensics
공식 코드Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection
https://github.com/ahaliassos/lipforensics
공식 코드Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection
https://github.com/ai-s2-lab/m2s-add
공식 코드Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
https://github.com/ai4ce/sscbench
공식 코드SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving
https://github.com/aibluefisher/dogs
공식 코드https://github.com/aimagelab/COCOFake
공식 코드Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
https://github.com/aimagelab/code
공식 코드Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities
https://github.com/aimi-lab/robust-pose-estimator
공식 코드Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
https://github.com/airlab-polimi/active-vision
공식 코드Enhancing Agricultural Environment Perception via Active Vision and Zero-Shot Learning
https://github.com/airou-lab/3d_multi_bird_tracking
공식 코드https://github.com/ais-lab/feat2map
공식 코드Fast and Lightweight Scene Regressor for Camera Relocalization
https://github.com/ajev-research/agg-layers-deepfake-detection
공식 코드https://github.com/ajhamdi/ges-splatting
공식 코드GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering
https://github.com/akashsengupta1997/hierarchicalprobabilistic3dhuman
공식 코드https://github.com/akirahero/diffindscene
공식 코드DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene Generation
https://github.com/akshaykburusa/gradientnbv
공식 코드Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes
https://github.com/aleasca/histology-nmj-and-muscle-activity-toolbox
공식 코드https://github.com/alexfengg/ste
공식 코드A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion
https://github.com/alexisbdr/e3d
공식 코드https://github.com/alexrich021/3dvnet
공식 코드3DVNet: Multi-View Depth Prediction and Volumetric Refinement
https://github.com/alibaba/cascade-stereo
공식 코드Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
https://github.com/amforever/fmfcc-a
공식 코드FMFCC-A: A Challenging Mandarin Dataset for Synthetic Speech Detection
https://github.com/amonszpart/SMASH
공식 코드SMASH: Physics-guided Reconstruction of Collisions from Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.