https://github.com/Ralami1859/Stochastic-Multi-Armed-Bandit
Thompson Sampling: An Asymptotically Optimal Finite Time Analysis
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/Ramneantu/deepfake-detection
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov3
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov4
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
https://github.com/RobotLocomotion/LabelFusion
LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes
https://github.com/RohanChacko/PeeledHuman
PeeledHuman: Robust Shape Representation for Textured 3D Human Body Reconstruction
https://github.com/SCPNet/Codes-for-SCPNet
https://github.com/ShanghaiTech-IMPACT/4DRGS
TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction
https://github.com/Shiaoming/ALIKE
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
https://github.com/ShunChengWu/SCFusion_Network
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
https://github.com/Skoltech-3D/sk3d_data
https://github.com/SunDaDenny/DuLa-Net
DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama
https://github.com/SusungHong/IF-DreamFusion
RealFusion: 360° Reconstruction of Any Object from a Single Image
https://github.com/SuyashSonawane/fakedetector
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/ToniRV/Kimera-VIO-Evaluation
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/UT-Austin-RPL/GIGA
Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations
https://github.com/UditSinghParihar/d2-net
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
https://github.com/UncleMEDM/PUGAN-pytorch
https://github.com/VladyslavUsenko/basalt-headers-mirror
https://github.com/YanLiqi/HAF
ASLFeat: Learning Local Features of Accurate Shape and Localization
https://github.com/YoYo000/MVSNet
https://github.com/YvanG/VQGAN-CLIP
https://github.com/ZhengZerong/DeepHuman
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
https://github.com/a-nau/Plane-Segmentation-Refinement
PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image
https://github.com/a-nau/easy-image-scraping
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
https://github.com/a-nau/image-selection-and-cnn-training
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
https://github.com/aghinsa/SketchTo3D
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
https://github.com/ahnobari/Range-GAN
https://github.com/aibluefisher/graphoptim
Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach
https://github.com/aiiu-lab/dfd-fcg
https://github.com/aimagelab/elsa_coco-fake
Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
https://github.com/aipixel/SG-SSC
https://github.com/airalcorn2/pytorch-nerf
https://github.com/ais-lab/d2s
Fast and Lightweight Scene Regressor for Camera Relocalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.