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 626–650 of 2326 papers

TitleStatusHype
Multi-View Large Reconstruction Model via Geometry-Aware Positional Encoding and Attention—0
NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images—0
HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object InteractionCode1
PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth EstimationCode5
GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement—0
Normal-guided Detail-Preserving Neural Implicit Function for High-Fidelity 3D Surface Reconstruction—0
3DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree Views—0
CMAR-Net: Accurate Cross-Modal 3D SAR Reconstruction of Vehicle Targets with Sparse-Aspect Multi-Baseline Data—0
Coarse-To-Fine Tensor Trains for Compact Visual RepresentationsCode1
C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT ReconstructionCode1
Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion—0
Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive DiffusionCode2
3D-HGS: 3D Half-Gaussian Splatting—0
MaGS: Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting—0
Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction—0
Physically Compatible 3D Object Modeling from a Single Image—0
Learning 3D Robotics Perception using Inductive Priors—0
A Pixel Is Worth More Than One 3D Gaussians in Single-View 3D Reconstruction—0
TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes—0
S^3Gaussian: Self-Supervised Street Gaussians for Autonomous DrivingCode4
Hierarchical Procedural Framework for Low-latency Robot-Assisted Hand-Object Interaction—0
Neural Radiance Fields for Novel View Synthesis in Monocular Gastroscopy—0
A Refined 3D Gaussian Representation for High-Quality Dynamic Scene Reconstruction—0
SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensing—0
3D Reconstruction with Fast Dipole Sums—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