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 2301–2326 of 2326 papers

TitleStatusHype
Floating No More: Object-Ground Reconstruction from a Single Image—0
Floorplan-Aware Camera Poses Refinement—0
FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation—0
Technical Report: Co-learning of geometry and semantics for online 3D mapping—0
FML: Face Model Learning from Videos—0
MinD-3D++: Advancing fMRI-Based 3D Reconstruction with High-Quality Textured Mesh Generation and a Comprehensive Dataset—0
fNeRF: High Quality Radiance Fields from Practical Cameras—0
Fostering Generalization in Single-view 3D Reconstruction by Learning a Hierarchy of Local and Global Shape Priors—0
FPGA-based Acceleration of Neural Network for Image Classification using Vitis AI—0
Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views—0
Tech Report: Divide and Conquer 3D Real-Time Reconstruction for Improved IGS—0
FreeCam3D: Snapshot Structured Light 3D with Freely-Moving Cameras—0
Free-DyGS: Camera-Pose-Free Scene Reconstruction based on Gaussian Splatting for Dynamic Surgical Videos—0
FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction—0
FreeStyleGAN: Free-view Editable Portrait Rendering with the Camera Manifold—0
Addressing the Shape-Radiance Ambiguity in View-Dependent Radiance Fields—0
Frequency-Based 3D Reconstruction of Transparent and Specular Objects—0
Frequency-based View Selection in Gaussian Splatting Reconstruction—0
Fristograms: Revealing and Exploiting Light Field Internals—0
FroDO: From Detections to 3D Objects—0
FroDO: From Detections to 3D Objects—0
From a few Accurate 2D Correspondences to 3D Point Clouds—0
Adaptive View Planning for Aerial 3D Reconstruction—0
From Flight to Insight: Semantic 3D Reconstruction for Aerial Inspection via Gaussian Splatting and Language-Guided Segmentation—0
From Low Field to High Value: Robust Cortical Mapping from Low-Field MRI—0
From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 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