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 1901–1925 of 2326 papers

TitleStatusHype
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for RoboticsCode0
Real-Time 3D Reconstruction of Colonoscopic Surfaces for Determining Missing Regions—0
Leveraging Vision Reconstruction Pipelines for Satellite Imagery—0
An Interactive Control Approach to 3D Shape Reconstruction—0
Higher Order Function Networks for View Planning and Multi-View Reconstruction—0
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping—0
PLMP - Point-Line Minimal Problems in Complete Multi-View Visibility—0
Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics—0
Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving—0
Unsupervised 3D Reconstruction Networks—0
X-Section: Cross-Section Prediction for Enhanced RGB-D Fusion—0
Track to Reconstruct and Reconstruct to TrackCode0
An Efficient Sampling-based Method for Online Informative Path Planning in Unknown EnvironmentsCode0
Weakly-Supervised 3D Pose Estimation from a Single Image using Multi-View Consistency—0
CvxNet: Learnable Convex Decomposition—0
DublinCity: Annotated LiDAR Point Cloud and its Applications—0
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video—0
Few-Shot Generalization for Single-Image 3D Reconstruction via Priors—0
Self-Supervised Deep Depth DenoisingCode0
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence—0
Learned Semantic Multi-Sensor Depth Map Fusion—0
Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction—0
Multi-Spectral Visual Odometry without Explicit Stereo Matching—0
Pro-Cam SSfM: Projector-Camera System for Structure and Spectral Reflectance from Motion—0
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single ImageCode0
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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