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 1276–1300 of 2326 papers

TitleStatusHype
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55—0
Large-Scale, Metric Structure From Motion for Unordered Light Fields—0
Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-Resolution Model for Multi-Class Volumetric Labeling—0
Large-scale visual SLAM for in-the-wild videos—0
3D Video Stabilization With Depth Estimation by CNN-Based Optimization—0
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies—0
Latent Radiance Fields with 3D-aware 2D Representations—0
latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction—0
LBurst: Learning-Based Robotic Burst Feature Extraction for 3D Reconstruction in Low Light—0
Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images—0
Learned Semantic Multi-Sensor Depth Map Fusion—0
Learning 3D Robotics Perception using Inductive Priors—0
Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification—0
Learning a Manifold as an Atlas—0
TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering—0
Learning Articulated Shape with Keypoint Pseudo-labels from Web Images—0
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey—0
Learning-Based Depth and Pose Estimation for Monocular Endoscope with Loss Generalization—0
Learning-based Monocular 3D Reconstruction of Birds: A Contemporary Survey—0
Learning-based Multi-View Stereo: A Survey—0
Learning-by-Synthesis for Appearance-based 3D Gaze Estimation—0
Learning Camera Movement Control from Real-World Drone Videos—0
Learning Complex 3D Human Self-Contact—0
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction—0
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping—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