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

Showing 526–550 of 2326 papers

TitleStatusHype
Efficient Implicit Neural Reconstruction Using LiDARCode1
E-3DGS: Gaussian Splatting with Exposure and Motion EventsCode1
Evaluate Geometry of Radiance Fields with Low-frequency Color PriorCode1
E3D: Event-Based 3D Shape ReconstructionCode1
EventEgo3D: 3D Human Motion Capture from Egocentric Event StreamsCode1
DeepSDF: Learning Continuous Signed Distance Functions for Shape RepresentationCode1
DeepShadow: Neural Shape from ShadowCode1
Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo viewsCode1
FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face ReconstructionCode1
DUDF: Differentiable Unsigned Distance Fields with Hyperbolic ScalingCode1
Doppelgangers: Learning to Disambiguate Images of Similar StructuresCode1
InFusionSurf: Refining Neural RGB-D Surface Reconstruction Using Per-Frame Intrinsic Refinement and TSDF Fusion Prior LearningCode1
A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic SurgeryCode1
Dynamic Plane Convolutional Occupancy NetworksCode1
DISORF: A Distributed Online 3D Reconstruction Framework for Mobile RobotsCode1
DITTO: Dual and Integrated Latent Topologies for Implicit 3D ReconstructionCode1
Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View SettingsCode1
Detecting Line Segments in Motion-blurred Images with EventsCode1
Fully Understanding Generic Objects: Modeling, Segmentation, and ReconstructionCode1
D2-Net: A Trainable CNN for Joint Detection and Description of Local FeaturesCode1
DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume ReconstructionCode1
E2GS: Event Enhanced Gaussian SplattingCode1
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRICode1
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional NetworkCode1
gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object ReconstructionCode1
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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