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 1901–1950 of 2326 papers

TitleStatusHype
Seafloor-Invariant Caustics Removal from Underwater Imagery—0
Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance—0
Second-order Anisotropic Gaussian Directional Derivative Filters for Blob Detection—0
Seeing A 3D World in A Grain of Sand—0
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints—0
SeFM: A Sequential Feature Point Matching Algorithm for Object 3D Reconstruction—0
Segmentation-Guided Neural Radiance Fields for Novel Street View Synthesis—0
Segment Based 3D Object Shape Priors—0
SegNeRF: 3D Part Segmentation with Neural Radiance Fields—0
AugGS: Self-augmented Gaussians with Structural Masks for Sparse-view 3D Reconstruction—0
VIN-NBV: A View Introspection Network for Next-Best-View Selection for Resource-Efficient 3D Reconstruction—0
Self-expressive Dictionary Learning for Dynamic 3D Reconstruction—0
Self-optimizing loop sifting and majorization for 3D reconstruction—0
SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting—0
Self-Supervised 3D Mesh Reconstruction From Single Images—0
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video—0
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors—0
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model—0
Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images—0
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions—0
VIO-Aided Structure from Motion Under Challenging Environments—0
Virtual Correspondence: Humans as a Cue for Extreme-View Geometry—0
Self-Supervised Surgical Instrument 3D Reconstruction from a Single Camera Image—0
Semantic 3D Reconstruction with Finite Element Bases—0
Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization—0
Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes—0
Semantically Informed Multiview Surface Refinement—0
Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle—0
Semantic Communication-Enabled Cloud-Edge-End-collaborative Metaverse Services Architecure—0
Semantic Multi-View Stereo: Jointly Estimating Objects and Voxels—0
SemanticSplat: Feed-Forward 3D Scene Understanding with Language-Aware Gaussian Fields—0
Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction—0
Semi-Calibrated Near Field Photometric Stereo—0
Zero-P-to-3: Zero-Shot Partial-View Images to 3D Object—0
Semi-Dense 3D Semantic Mapping from Monocular SLAM—0
Semi-supervised Dense Keypoints Using Unlabeled Multiview Images—0
Vision-Language Embodiment for Monocular Depth Estimation—0
SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing—0
S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in Remote Sensing Applications—0
Separating Overlapping Tissue Layers from Microscopy Images—0
Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model—0
Vision Transformers: State of the Art and Research Challenges—0
Set Distribution Networks: a Generative Model for Sets of Images—0
SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image—0
SHaDe: Compact and Consistent Dynamic 3D Reconstruction via Tri-Plane Deformation and Latent Diffusion—0
Shading Meets Motion: Self-supervised Indoor 3D Reconstruction Via Simultaneous Shape-from-Shading and Structure-from-Motion—0
Shadow Art Revisited: A Differentiable Rendering Based Approach—0
Shadows Shed Light on 3D Objects—0
Visual and Object Geo-localization: A Comprehensive Survey—0
ShapeGraFormer: GraFormer-Based Network for Hand-Object Reconstruction from a Single Depth Map—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