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 826–850 of 2326 papers

TitleStatusHype
SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization—0
3D Reconstruction of Interacting Multi-Person in Clothing from a Single Image—0
Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic SurgeryCode2
AGG: Amortized Generative 3D Gaussians for Single Image to 3D—0
A Survey on 3D Gaussian SplattingCode2
RHOBIN Challenge: Reconstruction of Human Object Interaction—0
Neural Rendering and Its Hardware Acceleration: A Review—0
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar ImageryCode1
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise—0
The More You See in 2D the More You Perceive in 3DCode2
CN-RMA: Combined Network with Ray Marching Aggregation for 3D Indoor Object Detection from Multi-view ImagesCode1
GES : Generalized Exponential Splatting for Efficient Radiance Field Rendering—0
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular VideoCode0
Sparse Views Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo—0
DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans—0
Slice3D: Multi-Slice Occlusion-Revealing Single View 3D Reconstruction—0
Coherence As Texture - Passive Textureless 3D Reconstruction by Self-interference—0
3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces—0
QUADify: Extracting Meshes with Pixel-level Details and Materials from Images—0
L0-Sampler: An L0 Model Guided Volume Sampling for NeRF—0
Informative Rays Selection for Few-Shot Neural Radiance Fields—0
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation—0
In-Hand 3D Object Reconstruction from a Monocular RGB Video—0
SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition—0
Objects as volumes: A stochastic geometry view of opaque solids—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