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 1201–1250 of 2326 papers

TitleStatusHype
MT3DNet: Multi-Task learning Network for 3D Surgical Scene Reconstruction—0
MTFusion: Reconstructing Any 3D Object from Single Image Using Multi-word Textual Inversion—0
Multi-Agent Embodied Question Answering in Interactive Environments—0
Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset—0
Multi-body Non-rigid Structure-from-Motion—0
Multicamera Calibration From Visible and Mirrored Epipoles—0
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images—0
Tracking-Aware Deformation Field Estimation for Non-rigid 3D Reconstruction in Robotic Surgeries—0
Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation—0
Multi-Modal 3D Mesh Reconstruction from Images and Text—0
Multimodal perception for dexterous manipulation—0
Multimodal Spatial Language Maps for Robot Navigation and Manipulation—0
Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions—0
Multi-Output Learning for Camera Relocalization—0
Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras—0
Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling—0
Multiresolution Deep Implicit Functions for 3D Shape Representation—0
Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance—0
Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering—0
Multi-Sensor Integration for Indoor 3D Reconstruction—0
Traffic Danger Recognition With Surveillance Cameras Without Training Data—0
Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction—0
Multi-Spectral Visual Odometry without Explicit Stereo Matching—0
Multistage SFM: A Coarse-to-Fine Approach for 3D Reconstruction—0
Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy—0
FLAME-based Multi-View 3D Face Reconstruction—0
Weighted Motion Averaging for the Registration of Multi-View Range Scans—0
Multi-View 3D Reconstruction With Transformers—0
Multi-view 3D Reconstruction with Transformer—0
Multi-view 3D surface reconstruction from SAR images by inverse rendering—0
Transientangelo: Few-Viewpoint Surface Reconstruction Using Single-Photon Lidar—0
3D Reconstruction using Structured Light from off-the-shelf components—0
Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People—0
Multiview Cross-supervision for Semantic Segmentation—0
Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction—0
Multi-view data capture using edge-synchronised mobiles—0
Multiview Differential Geometry of Curves—0
Multi-view Geometry: Correspondences Refinement Based on Algebraic Properties—0
Multiview Image-Based Localization—0
Multi-View Inpainting for RGB-D Sequence—0
3D Reconstruction through Fusion of Cross-View Images—0
Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments—0
TranSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Transformers—0
Multi-View Non-Rigid Refinement and Normal Selection for High Quality 3D Reconstruction—0
Trapping Light for Time of Flight—0
Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism—0
Multiview Rectification of Folded Documents—0
Multi-View Stereo Representation Revisit: Region-Aware MVSNet—0
Multi view stereo with semantic priors—0
Multi-View Stereo with Single-View Semantic Mesh Refinement—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