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 2226–2250 of 2326 papers

TitleStatusHype
Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the WildCode0
Tracking and triangulating firefly flashes in field recordingsCode0
Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?Code0
SMASH: Physics-guided Reconstruction of Collisions from VideosCode0
Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using Deep LearningCode0
SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D ScansCode0
Synthesizing Consistent Novel Views via 3D Epipolar Attention without Re-TrainingCode0
EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View StereoCode0
DeepVoxels: Learning Persistent 3D Feature EmbeddingsCode0
Learning Non-Volumetric Depth Fusion Using Successive ReprojectionsCode0
Heightmap Reconstruction of Macula on Color Fundus Images Using Conditional Generative Adversarial NetworksCode0
Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc cloudsCode0
Learning Object Manipulation Skills from Video via Approximate Differentiable PhysicsCode0
Compressing 3D Gaussian Splatting by Noise-Substituted Vector QuantizationCode0
Category-level Neural Field for Reconstruction of Partially Observed Objects in Indoor EnvironmentCode0
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape OptimizationCode0
Learning Partonomic 3D Reconstruction from Image CollectionsCode0
PU-GAN: a Point Cloud Upsampling Adversarial NetworkCode0
EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual HullsCode0
Multi-Cali Anything: Dense Feature Multi-Frame Structure-from-Motion for Large-Scale Camera Array CalibrationCode0
PU-GCN: Point Cloud Upsampling using Graph Convolutional NetworksCode0
Learning single-image 3D reconstruction by generative modelling of shape, pose and shadingCode0
Highlighting objects of interest in an image by integrating saliency and depthCode0
Learning Single-View 3D Reconstruction with Limited Pose SupervisionCode0
Domain-Adaptive Single-View 3D ReconstructionCode0
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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