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 601–625 of 2326 papers

TitleStatusHype
A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape FunctionsCode0
EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting—0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
SpotlessSplats: Ignoring Distractors in 3D Gaussian SplattingCode3
LiverUSRecon: Automatic 3D Reconstruction and Volumetry of the Liver with a Few Partial Ultrasound Scans—0
GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lighting—0
Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative ModelingCode1
On Scaling Up 3D Gaussian Splatting TrainingCode4
Crowd-Sourced NeRF: Collecting Data from Production Vehicles for 3D Street View Reconstruction—0
Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction—0
psPRF:Pansharpening Planar Neural Radiance Field for Generalized 3D Reconstruction Satellite Imagery—0
E2GS: Event Enhanced Gaussian SplattingCode1
MVSBoost: An Efficient Point Cloud-based 3D Reconstruction—0
FAWN: Floor-And-Walls Normal Regularization for Direct Neural TSDF Reconstruction—0
Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting—0
fNeRF: High Quality Radiance Fields from Practical Cameras—0
CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference—0
EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation ModelsCode2
A Two-Stage Masked Autoencoder Based Network for Indoor Depth CompletionCode1
Grounding Image Matching in 3D with MASt3RCode7
Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset—0
LRM-Zero: Training Large Reconstruction Models with Synthesized DataCode2
OpenMaterial: A Comprehensive Dataset of Complex Materials for 3D Reconstruction—0
Human-3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models—0
Category-level Neural Field for Reconstruction of Partially Observed Objects in Indoor EnvironmentCode0
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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