SOTAVerified

3D Reconstruction

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

Papers

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Showing 2151–2200 of 2326 papers

TitleStatusHype
DreamTexture: Shape from Virtual Texture with Analysis by Augmentation—0
Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics—0
DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction—0
Drone-based AI and 3D Reconstruction for Digital Twin Augmentation—0
DroNeRF: Real-time Multi-agent Drone Pose Optimization for Computing Neural Radiance Fields—0
DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery—0
Drought Stress Classification using 3D Plant Models—0
DSG-World: Learning a 3D Gaussian World Model from Dual State Videos—0
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models—0
DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
Survey on Fundamental Deep Learning 3D Reconstruction Techniques—0
A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction—0
DUST: Dual Union of Spatio-Temporal Subspaces for Monocular Multiple Object 3D Reconstruction—0
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP—0
Dynamic NeRF: A Review—0
Survey on Monocular Metric Depth Estimation—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid—0
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues—0
Survey on Semantic Stereo Matching / Semantic Depth Estimation—0
3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications—0
Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation—0
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition—0
EasyVis2: A Real Time Multi-view 3D Visualization for Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose—0
Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction—0
Edge Preserving Implicit Surface Representation of Point Clouds—0
Edge-preserving Near-light Photometric Stereo with Neural Surfaces—0
Edge Projection-Based Adaptive View Selection for Cone-Beam CT—0
Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting—0
Efficient 3D Reconstruction, Streaming and Visualization of Static and Dynamic Scene Parts for Multi-client Live-telepresence in Large-scale Environments—0
Efficient 3D Room Shape Recovery From a Single Panorama—0
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation—0
Efficient Global Optimization of Non-differentiable, Symmetric Objectives for Multi Camera Placement—0
Efficient Image-Space Extraction and Representation of 3D Surface Topography—0
X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics—0
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials—0
Semihierarchical Reconstruction and Weak-area Revisiting for Robotic Visual Seafloor Mapping—0
A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot—0
Efficient moving point handling for incremental 3D manifold reconstruction—0
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction—0
SyncDreamer for 3D Reconstruction of Endangered Animal Species with NeRF and NeuS—0
Efficient Second-Order Plane Adjustment—0
Efficient View Clustering and Selection for City-Scale 3D Reconstruction—0
A Framework for the Volumetric Integration of Depth Images—0
EGGS: Edge Guided Gaussian Splatting for Radiance Fields—0
Ego3DT: Tracking Every 3D Object in Ego-centric Videos—0
A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography—0
EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera—0
Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery—0
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All datasetsDTUShapeNetData3D−R2N2Scan2CAD3DPeopleAria Digital Twin DatasetAria Synthetic EnvironmentsScanNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
13D-R2N2Overall0.63—Unverified
2GipumaOverall0.58—Unverified
3COLMAPOverall0.53—Unverified
4MVSNetOverall0.46—Unverified
5Vis-MVSNetOverall0.37—Unverified
6AA-RMVSNetOverall0.36—Unverified
7Cas-MVSNetOverall0.36—Unverified
8EPP-MVSNetOverall0.36—Unverified
9PatchmatchNetOverall0.35—Unverified
10CVP-MVSNetOverall0.35—Unverified
#ModelMetricClaimedVerifiedStatus
1MD-GONIoU92.8—Unverified
2POCOIoU92.6—Unverified
3FS-SDFIoU91.2—Unverified
4DP-ConvONetIoU89.5—Unverified
5ConvONetIoU88.4—Unverified
6ONetIoU76.1—Unverified
7EVolTIoU73.8—Unverified
8ZubicLioIoU65.43—Unverified
#ModelMetricClaimedVerifiedStatus
1AttSets3DIoU0.64—Unverified
2PSGN3DIoU0.64—Unverified
3OGN3DIoU0.6—Unverified
43D-R2N23DIoU0.56—Unverified
#ModelMetricClaimedVerifiedStatus
1Scan2CADAverage Accuracy31.68—Unverified
23DMatchAverage Accuracy10.29—Unverified
#ModelMetricClaimedVerifiedStatus
1SVCPChamfer10—Unverified
#ModelMetricClaimedVerifiedStatus
1EVLAccuracy18.2—Unverified
#ModelMetricClaimedVerifiedStatus
1EVLAccuracy5.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)3DIoU89.4—Unverified