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 1501–1550 of 2326 papers

TitleStatusHype
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in VideosCode1
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessorCode0
Neural Fields as Learnable Kernels for 3D Reconstruction—0
Gradient-SDF: A Semi-Implicit Surface Representation for 3D ReconstructionCode1
VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance FieldCode1
Monocular Road Planar Parallax Estimation—0
Neural Fields in Visual Computing and BeyondCode2
ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction—0
Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One—0
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view StereoCode1
Pipeline for 3D reconstruction of the human body from AR/VR headset mounted egocentric cameras—0
Towards Panoptic 3D Parsing for Single Image in the Wild—0
FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face ReconstructionCode1
Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements—0
A Comparative Study of Coarse to Dense 3D Indoor Scene Registration Algorithms—0
UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D ModelCode1
TMBuD: A dataset for urban scene building detectionCode0
Robust Multi-view Registration of Point Sets with Laplacian Mixture Model—0
High Fidelity 3D Reconstructions with Limited Physical Views—0
AIR-Nets: An Attention-Based Framework for Locally Conditioned Implicit RepresentationsCode1
IVS3D: An Open Source Framework for Intelligent Video Sampling and Preprocessing to Facilitate 3D ReconstructionCode1
Style Agnostic 3D Reconstruction via Adversarial Style Transfer—0
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric MappingCode1
3D-RETR: End-to-End Single and Multi-View 3D Reconstruction with TransformersCode1
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks—0
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the WildCode1
Hyperspectral 3D Mapping of Underwater Environments—0
Exploiting 3D Shape Bias towards Robust Vision—0
ABO: Dataset and Benchmarks for Real-World 3D Object UnderstandingCode1
Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo—0
Automatic annotation of visual deep neural networks—0
Camera Calibration through Camera Projection LossCode1
TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph NetworkCode0
Topologically Consistent Multi-View Face Inference Using Volumetric Sampling—0
Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction—0
3D-MOV: Audio-Visual LSTM Autoencoder for 3D Reconstruction of Multiple Objects from Video—0
Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the WildCode1
Geon3D: Exploiting 3D Shape Bias towards Building Robust Machine Vision—0
Identity-Expression Ambiguity in 3D Morphable Face Models—0
PDC-Net+: Enhanced Probabilistic Dense Correspondence NetworkCode1
Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation—0
Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images—0
Survey on Semantic Stereo Matching / Semantic Depth Estimation—0
FreeStyleGAN: Free-view Editable Portrait Rendering with the Camera Manifold—0
Semi-supervised Dense Keypoints Using Unlabeled Multiview Images—0
DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar—0
ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations—0
3D Annotation Of Arbitrary Objects In The Wild—0
Multiresolution Deep Implicit Functions for 3D Shape Representation—0
Reconstructing and grounding narrated instructional videos in 3D—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