Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/xieyuankun/Codecfake
공식 코드The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio
https://github.com/xieyuankun/all-type-add
공식 코드Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception
https://github.com/xieyuankun/alm-add
공식 코드Does Current Deepfake Audio Detection Model Effectively Detect ALM-based Deepfake Audio?
https://github.com/xieyuankun/fsd-dataset
공식 코드https://github.com/xieyuankun/st-codecfake
공식 코드Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition
https://github.com/xjchengit/singgraph
공식 코드https://github.com/xjqi/GeoNet
공식 코드https://github.com/xmed-lab/C2RV-CBCT
공식 코드C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
https://github.com/xrenaa/CS-DisMo
공식 코드Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement
https://github.com/xrkong/skimba
공식 코드Skip Mamba Diffusion for Monocular 3D Semantic Scene Completion
https://github.com/xubaixinxbx/3dheads
공식 코드https://github.com/xubaixinxbx/gsurf
공식 코드GSurf: 3D Reconstruction via Signed Distance Fields with Direct Gaussian Supervision
https://github.com/xubaixinxbx/high-fidelity-3d-reconstruction-of-human-heads
공식 코드https://github.com/xucao-42/mvas
공식 코드https://github.com/xuchen-ethz/fast-snarf
공식 코드https://github.com/xuebinqin/DIS
공식 코드https://github.com/xuechaochen/p3p-mae
공식 코드P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders
https://github.com/xuelunshen/gim
공식 코드GIM: Learning Generalizable Image Matcher From Internet Videos
https://github.com/xujiacong/thermal-nerf
공식 코드Leveraging Thermal Modality to Enhance Reconstruction in Low-Light Conditions
https://github.com/xuxy09/texformer
공식 코드3D Human Texture Estimation from a Single Image with Transformers
https://github.com/xuyingzhongguo/deepfakeannotations
공식 코드Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
https://github.com/xuyingzhongguo/mcx-api
공식 코드Learning Pairwise Interaction for Generalizable DeepFake Detection
https://github.com/xuyingzhongguo/vod
공식 코드VoD: Learning Volume of Differences for Video-Based Deepfake Detection
https://github.com/xxlong0/CNMNet
공식 코드Occlusion-Aware Depth Estimation with Adaptive Normal Constraints
https://github.com/yanghb22-fdu/hi3d-official
공식 코드Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models
https://github.com/yanx27/JS3C-Net
공식 코드https://github.com/yaoyx689/Fast-Robust-ICP
공식 코드https://github.com/yaselley/ssl_layerwise_deepfake
공식 코드Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection
https://github.com/ybzh/masksurf
공식 코드Masked Surfel Prediction for Self-Supervised Point Cloud Learning
https://github.com/yccyenchicheng/SDFusion
공식 코드SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation
https://github.com/yefanzhou/dispersion-score
공식 코드https://github.com/yerfor/Real3DPortrait
공식 코드Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis
https://github.com/yermandy/deepfake-detection
공식 코드Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection
https://github.com/yfujimura/DCV-release
공식 코드https://github.com/yhw-yhw/D2HC-RMVSNet
공식 코드Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.