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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1125 of 2326 papers

TitleStatusHype
Bayesian Fields: Task-driven Open-Set Semantic Gaussian Splatting—0
Drone-based AI and 3D Reconstruction for Digital Twin Augmentation—0
DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction—0
A comprehensive survey on recent deep learning-based methods applied to surgical data—0
Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes—0
Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics—0
A comprehensive survey on point cloud registration—0
DreamTexture: Shape from Virtual Texture with Analysis by Augmentation—0
BAGS: Building Animatable Gaussian Splatting from a Monocular Video with Diffusion Priors—0
3D Reconstruction from Full-view Fisheye Camera—0
Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction—0
LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives—0
Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding—0
LiM-Loc: Visual Localization with Dense and Accurate 3D Reference Maps Directly Corresponding 2D Keypoints to 3D LiDAR Point Clouds—0
DreaMo: Articulated 3D Reconstruction From A Single Casual Video—0
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation—0
A Comparison of Tiny-nerf versus Spatial Representations for 3d Reconstruction—0
Do We Need Binary Features for 3D Reconstruction?—0
Back to RGB: 3D tracking of hands and hand-object interactions based on short-baseline stereo—0
3D Reconstruction from Accidental Motion—0
Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features—0
Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction—0
Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data—0
A Comparative Study of Coarse to Dense 3D Indoor Scene Registration Algorithms—0
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis—0
Show:102550
← PrevPage 45 of 94Next →
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