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 226–250 of 2326 papers

TitleStatusHype
IM360: Textured Mesh Reconstruction for Large-scale Indoor Mapping with 360^ Cameras—0
ROI-NeRFs: Hi-Fi Visualization of Objects of Interest within a Scene by NeRFs Composition—0
No-reference geometry quality assessment for colorless point clouds via list-wise rank learningCode0
Multi-view 3D surface reconstruction from SAR images by inverse rendering—0
HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation—0
X-SG^2S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks—0
PUGS: Perceptual Uncertainty for Grasp Selection in Underwater Environments—0
Large Images are Gaussians: High-Quality Large Image Representation with Levels of 2D Gaussian SplattingCode1
Latent Radiance Fields with 3D-aware 2D Representations—0
TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow ModelsCode5
SC-OmniGS: Self-Calibrating Omnidirectional Gaussian Splatting—0
sshELF: Single-Shot Hierarchical Extrapolation of Latent Features for 3D Reconstruction from Sparse-Views—0
Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics—0
Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using Deep LearningCode0
A Real-Time Human Pose Measurement System for Human-In-The-Loop Dynamic Simulators—0
SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification—0
GP-GS: Gaussian Processes for Enhanced Gaussian SplattingCode1
Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding—0
3D Reconstruction of Shoes for Augmented Reality—0
Consistency Diffusion Models for Single-Image 3D Reconstruction with Priors—0
3D Reconstruction of non-visible surfaces of objects from a Single Depth View -- Comparative Study—0
Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling—0
Acquiring Submillimeter-Accurate Multi-Task Vision Datasets for Computer-Assisted Orthopedic SurgeryCode0
Light3R-SfM: Towards Feed-forward Structure-from-Motion—0
Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 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