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 776–800 of 2326 papers

TitleStatusHype
Pooling Image Datasets With Multiple Covariate Shift and Imbalance—0
TripoSR: Fast 3D Object Reconstruction from a Single ImageCode9
G3DR: Generative 3D Reconstruction in ImageNetCode1
DISORF: A Distributed Online 3D Reconstruction Framework for Mobile RobotsCode1
WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model ResearchCode1
CharNeRF: 3D Character Generation from Concept Art—0
Sora Generates Videos with Stunning Geometrical Consistency—0
CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention—0
GenNBV: Generalizable Next-Best-View Policy for Active 3D ReconstructionCode2
A Generative Machine Learning Model for Material Microstructure 3D Reconstruction and Performance Evaluation—0
Cameras as Rays: Pose Estimation via Ray DiffusionCode4
MVD^2: Efficient Multiview 3D Reconstruction for Multiview Diffusion—0
MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction—0
A Robust Error-Resistant View Selection Method for 3D Reconstruction—0
DiffPoint: Single and Multi-view Point Cloud Reconstruction with ViT Based Diffusion Model—0
Dense Matchers for Dense Tracking—0
GIM: Learning Generalizable Image Matcher From Internet VideosCode4
Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions—0
GES: Generalized Exponential Splatting for Efficient Radiance Field RenderingCode3
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving EnvironmentsCode2
DUDF: Differentiable Unsigned Distance Fields with Hyperbolic ScalingCode1
Camera Calibration through Geometric Constraints from Rotation and Projection MatricesCode1
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation—0
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving—0
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction—0
Show:102550
← PrevPage 32 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