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 2201–2250 of 2326 papers

TitleStatusHype
Graph-Based Parallel Large Scale Structure from MotionCode0
Probabilistic Inference for Camera Calibration in Light Microscopy under Circular MotionCode0
GraphX-Convolution for Point Cloud Deformation in 2D-to-3D ConversionCode0
UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic ImagesCode0
Sequential Experimental Design for X-Ray CT Using Deep Reinforcement LearningCode0
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single ImageCode0
Learning Character-Agnostic Motion for Motion Retargeting in 2DCode0
Scan2CAD: Learning CAD Model Alignment in RGB-D ScansCode0
VoloGAN: Adversarial Domain Adaptation for Synthetic Depth DataCode0
Neural RGB->D Sensing: Depth and Uncertainty from a Video CameraCode0
TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic SceneCode0
Superhuman Accuracy on the SNEMI3D Connectomics ChallengeCode0
MOTChallenge 2015: Towards a Benchmark for Multi-Target TrackingCode0
The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-caseCode0
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground TruthCode0
Learning Embedding of 3D models with Quadric LossCode0
The Double Sphere Camera ModelCode0
Learning Free-Form Deformations for 3D Object ReconstructionCode0
3DGSR: Implicit Surface Reconstruction with 3D Gaussian SplattingCode0
Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRICode0
3D reconstruction using Structure for MotionCode0
MPF-GAN: an enhanced architecture for 3D face reconstructionCode0
Data-Driven 3D Reconstruction of Dressed Humans From Sparse ViewsCode0
Learning Implicit Fields for Generative Shape ModelingCode0
Weakly supervised 3D Reconstruction with Adversarial ConstraintCode0
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