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 1426–1450 of 2326 papers

TitleStatusHype
3D Target Detection and Spectral Classification for Single-photon LiDAR Data—0
A Simplified 3D Ultrasound Freehand Imaging Framework Using 1D Linear Probe and Low-Cost Mechanical Track—0
HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces—0
Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM—0
3D Colored Shape Reconstruction from a Single RGB Image through Diffusion—0
3D reconstruction from spherical images: A review of techniques, applications, and prospects—0
3D genome reconstruction from partially phased Hi-C dataCode0
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning—0
A Comparison of Tiny-nerf versus Spatial Representations for 3d Reconstruction—0
Local Feature Extraction from Salient Regions by Feature Map Transformation—0
3D Reconstruction of Non-cooperative Resident Space Objects using Instant NGP-accelerated NeRF and D-NeRF—0
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP—0
Regularizing disparity estimation via multi task learning with structured light reconstruction—0
Edge Preserving Implicit Surface Representation of Point Clouds—0
Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery—0
Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3D Ultrasound Imaging System—0
Towards a Pipeline for Real-Time Visualization of Faces for VR-based Telepresence and Live Broadcasting Utilizing Neural RenderingCode0
BS3D: Building-scale 3D Reconstruction from RGB-D Images—0
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition—0
RealFusion: 360deg Reconstruction of Any Object From a Single Image—0
Neural Kaleidoscopic Space Sculpting—0
Dionysus: Recovering Scene Structures by Dividing Into Semantic Pieces—0
Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes—0
DG-Recon: Depth-Guided Neural 3D Scene Reconstruction—0
DiLiGenT-Pi: Photometric Stereo for Planar Surfaces with Rich Details - Benchmark Dataset and Beyond—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