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 1751–1800 of 2326 papers

TitleStatusHype
3DeepCT: Learning Volumetric Scattering Tomography of Clouds—0
Adaptive Surface Reconstruction With Multiscale Convolutional Kernels—0
Uncertainty-Aware Human Mesh Recovery From Video by Learning Part-Based 3D Dynamics—0
Gaussian Fusion: Accurate 3D Reconstruction via Geometry-Guided Displacement Interpolation—0
Multi-View 3D Reconstruction With Transformers—0
In-the-Wild Single Camera 3D Reconstruction Through Moving Water Surfaces—0
Just a Few Points Are All You Need for Multi-View Stereo: A Novel Semi-Supervised Learning Method for Multi-View Stereo—0
Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning—0
Learning Inter- and Intraframe Representations for Non-Lambertian Photometric Stereo—0
From Points to Multi-Object 3D Reconstruction—0
Towards Automatic Digital Documentation and Progress Reporting of Mechanical Construction Pipes using Smartphones—0
Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation—0
Self-supervised monocular depth estimation from oblique UAV videosCode0
Learning Complex 3D Human Self-Contact—0
Reconstructing Hand-Object Interactions in the Wild—0
D^2IM-Net: Learning Detail Disentangled Implicit Fields from Single Images—0
3D Scattering Tomography by Deep Learning with Architecture Tailored to Cloud Fields—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
Online Adaptation for Consistent Mesh Reconstruction in the Wild—0
A grid-point detection method based on U-net for a structured light system—0
Automated Calibration of Mobile Cameras for 3D Reconstruction of Mechanical Pipes—0
Isometric Multi-Shape Matching—0
3D-NVS: A 3D Supervision Approach for Next View Selection—0
Facetwise Mesh Refinement for Multi-View Stereo—0
Where to Explore Next? ExHistCNN for History-aware Autonomous 3D ExplorationCode0
Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction—0
Efficient Scene Compression for Visual-based LocalizationCode0
Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale—0
Three-dimensional Segmentation of the Scoliotic Spine from MRI using Unsupervised Volume-based MR-CT Synthesis—0
Independent Sign Language Recognition with 3D Body, Hands, and Face Reconstruction—0
Dehazing Cost Volume for Deep Multi-view Stereo in Scattering Media with Airlight and Scattering Coefficient EstimationCode0
Relative Drone-Ground Vehicle Localization using LiDAR and Fisheye Cameras through Direct and Indirect Observations—0
Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern—0
GPR-based Model Reconstruction System for Underground Utilities Using GPRNet—0
Realtime CNN-based Keypoint Detector with Sobel Filter and CNN-based Descriptor Trained with Keypoint Candidates—0
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data—0
3D photogrammetry point cloud segmentation using a model ensembling framework—0
Improving the generalization of network based relative pose regression: dimension reduction as a regularizer—0
Learning to Guide Local Feature Matches—0
GAMesh: Guided and Augmented Meshing for Deep Point NetworksCode0
Probabilistic 3D surface reconstruction from sparse MRI information—0
A simulation environment for drone cinematography—0
Image-based underwater 3D reconstruction for Cultural Heritage: from image collection to 3D. Critical steps and considerations—0
3D Dense Geometry-Guided Facial Expression Synthesis by Adversarial Learning—0
Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People—0
Loop-box: Multi-Agent Direct SLAM Triggered by Single Loop Closure for Large-Scale Mapping—0
6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN—0
3D Reconstruction and Segmentation of Dissection Photographs for MRI-free NeuropathologyCode0
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors—0
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems—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