Papers with Code 아카이브

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/Leminhbinh0209/AAAI22-ADD

공식 코드

ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images

https://github.com/LetterLiGo/SafeEar

공식 코드

SafeEar: Content Privacy-Preserving Audio Deepfake Detection

https://github.com/LifeBeyondExpectations/PointMixer

공식 코드

PointMixer: MLP-Mixer for Point Cloud Understanding

https://github.com/Linxius/UrbanScene3D

공식 코드

Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset

https://github.com/MIT-SPARK/Kimera

공식 코드

Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities

https://github.com/MZMMSEC/SJEDD

공식 코드

Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach

https://github.com/NASA-AMMOS/VECTOR

공식 코드

Opening the Black Box of 3D Reconstruction Error Analysis with VECTOR

https://github.com/NVlabs/BundleSDF

공식 코드

BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

https://github.com/NVlabs/InstantSplat

공식 코드

InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds

https://github.com/NVlabs/LSM

공식 코드

Large Spatial Model: End-to-end Unposed Images to Semantic 3D

https://github.com/NVlabs/nvdiffrecmc

공식 코드

Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising

https://github.com/NetaGlazer/ADD-GP

공식 코드

Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes

https://github.com/NickyFot/HitchhikersGuide

공식 코드

A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning

https://github.com/NikolaZubic/2dimageto3dmodel

공식 코드

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering

https://github.com/One-2-3-45/One-2-3-45

공식 코드

One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization

https://github.com/OpenGVLab/PonderV2

공식 코드

PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

https://github.com/OpenRL-Lab/DeepFakeFace

공식 코드

Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models

https://github.com/OpenTAI/wild-deepfake

공식 코드

WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection

https://github.com/Ouasfi/FS-SDF

공식 코드

Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space

https://github.com/PJaramilloV/pcdiff-method

공식 코드

Cultural Heritage 3D Reconstruction with Diffusion Networks

https://github.com/POSTECH-CVLab/PeRFception

공식 코드

PeRFception: Perception using Radiance Fields

https://github.com/PalomaCantero/Deepfake-Detection-and-the-Impact-of-Limited-Computing-Capabilities

공식 코드

Deepfake Detection and the Impact of Limited Computing Capabilities

https://github.com/PeymanTahghighi/FundusDeepLearning

공식 코드

Heightmap Reconstruction of Macula on Color Fundus Images Using Conditional Generative Adversarial Networks

https://github.com/PinataFarms/DAD-3DHeads

공식 코드

DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image

https://github.com/PrincetonUniversity/specvols

공식 코드

Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes

https://github.com/PruneTruong/DenseMatching

공식 코드

Learning Accurate Dense Correspondences and When to Trust Them

https://github.com/PruneTruong/PDCNet

공식 코드

Learning Accurate Dense Correspondences and When to Trust Them

https://github.com/Qi-Yangsjtu/MPED

공식 코드

MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy

https://github.com/ROKIT-Healthcare/FastSurf

공식 코드

InFusionSurf: Refining Neural RGB-D Surface Reconstruction Using Per-Frame Intrinsic Refinement and TSDF Fusion Prior Learning

https://github.com/RainWang6188/Monovision-3D-Reconstruction-System

공식 코드

3D Reconstruction Using a Linear Laser Scanner and a Camera

https://github.com/Regenerator/prns

공식 코드

Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image

https://github.com/RuijieZhu94/MotionGS

공식 코드

MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting

https://github.com/RuojinCai/Doppelgangers

공식 코드

Doppelgangers: Learning to Disambiguate Images of Similar Structures

https://github.com/SBCV/SatelliteSurfaceReconstruction

공식 코드

3D Surface Reconstruction From Multi-Date Satellite Images

https://github.com/SadilKhan/MARVEL-FX3D

공식 코드

MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation

https://github.com/Sangluisme/curverture-sample-uncertainty-aware-sdf

공식 코드

Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations

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