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 1951–1975 of 2326 papers

TitleStatusHype
HoloPose: Holistic 3D Human Reconstruction In-The-Wild—0
Learning Non-Volumetric Depth Fusion Using Successive ReprojectionsCode0
Neural RGB(r)D Sensing: Depth and Uncertainty From a Video Camera—0
StereoDRNet: Dilated Residual StereoNet—0
D2-Net: A Trainable CNN for Joint Description and Detection of Local FeaturesCode0
3D Reconstruction of Whole Stomach from Endoscope Video Using Structure-from-Motion—0
DISN: Deep Implicit Surface Network for High-quality Single-view 3D ReconstructionCode0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes—0
Bi-objective Framework for Sensor Fusion in RGB-D Multi-View Systems: Applications in Calibration—0
Robust Point Cloud Based Reconstruction of Large-Scale Outdoor ScenesCode0
Separating Overlapping Tissue Layers from Microscopy Images—0
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model—0
Learning Perspective Undistortion of Portraits—0
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping—0
Disparity-Augmented Trajectories for Human Activity Recognition—0
Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data—0
On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes—0
What Do Single-view 3D Reconstruction Networks Learn?—0
Endoscopy artifact detection (EAD 2019) challenge dataset—0
Learning Character-Agnostic Motion for Motion Retargeting in 2DCode0
Single Image 3D Hand Reconstruction with Mesh ConvolutionsCode0
IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction—0
RevealNet: Seeing Behind Objects in RGB-D Scans—0
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction—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