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 401–425 of 2326 papers

TitleStatusHype
3D Reconstruction by Looking: Instantaneous Blind Spot Detector for Indoor SLAM through Mixed Reality—0
M3D: Dual-Stream Selective State Spaces and Depth-Driven Framework for High-Fidelity Single-View 3D ReconstructionCode0
TimeFormer: Capturing Temporal Relationships of Deformable 3D Gaussians for Robust ReconstructionCode1
Towards Degradation-Robust Reconstruction in Generalizable NeRF—0
BVI-CR: A Multi-View Human Dataset for Volumetric Video CompressionCode0
DiHuR: Diffusion-Guided Generalizable Human Reconstruction—0
ARM: Appearance Reconstruction Model for Relightable 3D Generation—0
Poster: Reliable 3D Reconstruction for Ad-hoc Edge Implementations—0
SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D ReconstructionCode2
The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods—0
USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian SplattingCode0
MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds—0
CropCraft: Inverse Procedural Modeling for 3D Reconstruction of Crop Plants—0
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model—0
Extreme Rotation Estimation in the Wild—0
Benchmarking 3D multi-coil NC-PDNet MRI reconstruction—0
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse ViewsCode3
GANESH: Generalizable NeRF for Lensless Imaging—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion—0
Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery—0
Next Best View For Point-Cloud Model Acquisition: Bayesian Approximation and Uncertainty Analysis—0
LBurst: Learning-Based Robotic Burst Feature Extraction for 3D Reconstruction in Low Light—0
No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed ImagesCode4
PointRecon: Online Point-based 3D Reconstruction via Ray-based 2D-3D Matching—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