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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1875 of 2326 papers

TitleStatusHype
SplatMAP: Online Dense Monocular SLAM with 3D Gaussian Splatting—0
Automated Calibration of Mobile Cameras for 3D Reconstruction of Mechanical Pipes—0
Automatic 2D-3D Registration without Contrast Agent during Neurovascular Interventions—0
Automatic 3D Reconstruction for Symmetric Shapes—0
Automatic 3D Reconstruction of Manifold Meshes via Delaunay Triangulation and Mesh Sweeping—0
Automatic annotation of visual deep neural networks—0
Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3D Ultrasound Imaging System—0
Automatic extraction and 3D reconstruction of split wire from point cloud data based on improved DPC algorithm—0
Automatic occlusion removal from 3D maps for maritime situational awareness—0
Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery—0
Autonomous 3D Reconstruction Using a MAV—0
AutoPartGen: Autogressive 3D Part Generation and Discovery—0
AutoRecon: Automated 3D Object Discovery and Reconstruction—0
Splats in Splats: Embedding Invisible 3D Watermark within Gaussian Splatting—0
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis—0
Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction—0
Back to RGB: 3D tracking of hands and hand-object interactions based on short-baseline stereo—0
BAGS: Building Animatable Gaussian Splatting from a Monocular Video with Diffusion Priors—0
Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes—0
3D Dense Geometry-Guided Facial Expression Synthesis by Adversarial Learning—0
Bayesian Fields: Task-driven Open-Set Semantic Gaussian Splatting—0
Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion—0
Benchmarking 3D multi-coil NC-PDNet MRI reconstruction—0
Betsu-Betsu: Multi-View Separable 3D Reconstruction of Two Interacting Objects—0
Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with Specularities and Shading—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