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 2201–2225 of 2326 papers

TitleStatusHype
Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion—0
EM-based approach to 3D reconstruction from single-waveform multispectral Lidar data—0
AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scene—0
Multi-tiling Neural Radiance Field (NeRF) -- Geometric Assessment on Large-scale Aerial Datasets—0
EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting—0
EndoGaussians: Single View Dynamic Gaussian Splatting for Deformable Endoscopic Tissues Reconstruction—0
EndoMetric: Near-Light Monocular Metric Scale Estimation in Endoscopy—0
Endoscopy artifact detection (EAD 2019) challenge dataset—0
EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting—0
End-to-end 3D shape inverse rendering of different classes of objects from a single input image—0
End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images—0
End-to-End Latency Optimization of Multi-view 3D Reconstruction for Disaster Response—0
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes—0
Energy Based Multi-model Fitting & Matching for 3D Reconstruction—0
Enforcing 3D Topological Constraints in Composite Objects via Implicit Functions—0
Enhanced fringe-to-phase framework using deep learning—0
Enhanced Multi-View Pedestrian Detection Using Probabilistic Occupancy Volume—0
Enhanced Stable View Synthesis—0
Enhancement of 3D Gaussian Splatting using Raw Mesh for Photorealistic Recreation of Architectures—0
VoxDepth: Rectification of Depth Images on Edge Devices—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
Enhancing Face Recognition With Self-Supervised 3D Reconstruction—0
Enhancing Neural Rendering Methods with Image Augmentations—0
Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation—0
Equivalent Constraints for Two-View Geometry: Pose Solution/Pure Rotation Identification and 3D 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