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3D Reconstruction

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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/chtsy/buol

공식 코드

BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single Image

https://github.com/chuangchuangtan/c2p-clip-deepfakedetection

공식 코드

C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection

https://github.com/chuangchuangtan/data-independent-operator

공식 코드

Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection

https://github.com/chuangchuangtan/freqnet-deepfakedetection

공식 코드

Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning

https://github.com/chuangchuangtan/npr-deepfakedetection

공식 코드

Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection

https://github.com/chufengxiao/DifferSketching

공식 코드

DifferSketching: How Differently Do People Sketch 3D Objects?

https://github.com/cicwi/recast3d

공식 코드

Real-Time Tilt Undersampling Optimization during Electron Tomography of Beam Sensitive Samples using Golden Ratio Scanning and RECAST3D

https://github.com/cipiorhei/tmbud

공식 코드

TMBuD: A dataset for urban scene building detection

https://github.com/cjeen/sketchsampler

공식 코드

SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Sampling

https://github.com/clad23/clad

공식 코드

CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning

https://github.com/clovaai/aasist

공식 코드

AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks

https://github.com/clpeng/Awesome-Face-Forgery-Generation-and-Detection

공식 코드

Countering Malicious DeepFakes: Survey, Battleground, and Horizon

https://github.com/cmlab-korea/awesome-3d-low-level-vision

공식 코드

R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision

https://github.com/continental/6img-to-3d

공식 코드

6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene Reconstruction

https://github.com/controlnet/av-deepfake1m

공식 코드

AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset

https://github.com/croitorualin/biodeep

공식 코드

Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook

https://github.com/crystalwlz/omegas

공식 코드

OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian Segmentation

https://github.com/cure-lab/DeciWatch

공식 코드

DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation

https://github.com/cv-rits/LMSCNet

공식 코드

LMSCNet: Lightweight Multiscale 3D Semantic Completion

https://github.com/cv-rits/MonoScene

공식 코드

MonoScene: Monocular 3D Semantic Scene Completion

https://github.com/cveo/s3net

공식 코드

S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery

https://github.com/cvg/NoPoSplat

공식 코드

No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images

https://github.com/cvg/lightglue

공식 코드

LightGlue: Local Feature Matching at Light Speed

https://github.com/cvg/pixel-perfect-sfm

공식 코드

Pixel-Perfect Structure-from-Motion with Featuremetric Refinement

https://github.com/cvlab-columbia/pix2gestalt

공식 코드

pix2gestalt: Amodal Segmentation by Synthesizing Wholes

https://github.com/cvlab-columbia/zero123

공식 코드

Zero-1-to-3: Zero-shot One Image to 3D Object

https://github.com/cvlab-epfl/adv_param_pose_prior

공식 코드

Adversarial Parametric Pose Prior

https://github.com/cvlab-kaist/PF3plat

공식 코드

PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting

https://github.com/cvlablums/cvgl-camera-calibration

공식 코드

Camera Calibration through Geometric Constraints from Rotation and Projection Matrices

https://github.com/cvmi-lab/total-decom

공식 코드

Total-Decom: Decomposed 3D Scene Reconstruction with Minimal Interaction

https://github.com/cy-xu/spatially_aware_ai

공식 코드

Multimodal 3D Fusion and In-Situ Learning for Spatially Aware AI

https://github.com/cyjie429/rego

공식 코드

Region-Based Optimization in Continual Learning for Audio Deepfake Detection

https://github.com/czq142857/BSP-NET-original

공식 코드

BSP-Net: Generating Compact Meshes via Binary Space Partitioning

https://github.com/czq142857/implicit-decoder

공식 코드

Learning Implicit Fields for Generative Shape Modeling

https://github.com/dabbrata/depth-estimation-enc-dec

공식 코드

Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth Estimation

https://github.com/dan8991/the-sound-of-silence

공식 코드

The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.