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 1576–1600 of 2326 papers

TitleStatusHype
Pre-train, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction—0
Gravitationally Lensed Black Hole Emission Tomography—0
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis—0
MonoTrack: Shuttle trajectory reconstruction from monocular badminton video—0
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence—0
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions—0
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian—0
Towards Learning Neural Representations from Shadows—0
RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformers—0
A Real World Dataset for Multi-view 3D Reconstruction—0
DiFT: Differentiable Differential Feature Transform for Multi-View Stereo—0
VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single and Multi-view 3D Reconstruction—0
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers—0
Addressing the Shape-Radiance Ambiguity in View-Dependent Radiance Fields—0
Sketched RT3D: How to reconstruct billions of photons per secondCode0
H4D: Human 4D Modeling by Learning Neural Compositional Representation—0
Object Pose Estimation using Mid-level Visual RepresentationsCode0
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse views—0
Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function—0
ERF: Explicit Radiance Field Reconstruction From Scratch—0
RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training—0
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression—0
MITI: SLAM Benchmark for Laparoscopic Surgery—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