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/lalithbharadwajbaru/wavelet-clip
공식 코드Wavelet-Driven Generalizable Framework for Deepfake Face Forgery Detection
https://github.com/lasinger/3d-fluid-flow
공식 코드3D Fluid Flow Estimation with Integrated Particle Reconstruction
https://github.com/lastbasket/surgtpgs
공식 코드SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
https://github.com/laughtervv/DISN
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/layumi/3D-Magic-Mirror
공식 코드3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective
https://github.com/leaffeall/DFS-GDD
공식 코드Decoupling Forgery Semantics for Generalizable Deepfake Detection
https://github.com/leminhbinh0209/add
공식 코드https://github.com/lh9171338/FE-LSD
공식 코드Detecting Line Segments in Motion-blurred Images with Events
https://github.com/liangchen527/sladd
공식 코드Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
https://github.com/lidq92/lsrn-pcgc
공식 코드Lightweight super resolution network for point cloud geometry compression
https://github.com/lifebeyondexpectations/eccv22-pointmixer
공식 코드https://github.com/lijx10/SO-Net
공식 코드https://github.com/likojack/bnv_fusion
공식 코드BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion
https://github.com/lin-jinwei/OneTo3D
공식 코드OneTo3D: One Image to Re-editable Dynamic 3D Model and Video Generation
https://github.com/lioryariv/idr
공식 코드Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
https://github.com/liren2515/dig
공식 코드https://github.com/liren2515/dmap
공식 코드Single View Garment Reconstruction Using Diffusion Mapping Via Pattern Coordinates
https://github.com/littlejuyan/df_fairness
공식 코드https://github.com/liu-tianchi/nes2net
공식 코드Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing
https://github.com/liubonan123/omnicolor
공식 코드https://github.com/liuquan98/apr
공식 코드APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction
https://github.com/liyingcv/long-range-grouping-transformer
공식 코드Long-Range Grouping Transformer for Multi-View 3D Reconstruction
https://github.com/ljj898/cmdfd-dataset-and-deepfake-detection
공식 코드Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
https://github.com/lkeab/gsnet
공식 코드GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
https://github.com/llukmancerkezi/roster
공식 코드https://github.com/lmb-freiburg/ogn
공식 코드Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
https://github.com/logsanand/Monocular-depth-UAVimages
공식 코드Self-supervised monocular depth estimation from oblique UAV videos
https://github.com/loong8888/WAIR
공식 코드https://github.com/lorenmt/vsl
공식 코드https://github.com/lpiccinelli-eth/UniK3D
공식 코드https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch
공식 코드Extremely Dense Point Correspondences using a Learned Feature Descriptor
https://github.com/lppllppl920/DenseReconstruction-Pytorch
공식 코드https://github.com/lrnzgiusti/marf
공식 코드https://github.com/lrse/sodm
공식 코드Mapping Semantic Segmentation to Point Clouds Using Structure from Motion for Forest Analysis
https://github.com/lsy0882/FakeMix
공식 코드WWW: Where, Which and Whatever Enhancing Interpretability in Multimodal Deepfake Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.