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 1901–1950 of 2326 papers

TitleStatusHype
BRUM: Robust 3D Vehicle Reconstruction from 360 Sparse Images—0
BS3D: Building-scale 3D Reconstruction from RGB-D Images—0
sshELF: Single-Shot Hierarchical Extrapolation of Latent Features for 3D Reconstruction from Sparse-Views—0
BU-CVKit: Extendable Computer Vision Framework for Species Independent Tracking and Analysis—0
BuFF: Burst Feature Finder for Light-Constrained 3D Reconstruction—0
Building Proteins in a Day: Efficient 3D Molecular Reconstruction—0
Building with Drones: Accurate 3D Facade Reconstruction using MAVs—0
Bundle Adjustment Revisited—0
SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot—0
A Multiple-View Geometric Model for Specularity Prediction on General Curved Surfaces—0
Burst Imaging for Light-Constrained Structure-From-Motion—0
SST: Real-time End-to-end Monocular 3D Reconstruction via Sparse Spatial-Temporal Guidance—0
CAD Priors for Accurate and Flexible Instance Reconstruction—0
CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention—0
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation—0
St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World—0
Stable Template-Based Isometric 3D Reconstruction in All Imaging Conditions by Linear Least-Squares—0
State of the Art in Dense Monocular Non-Rigid 3D Reconstruction—0
STD-Net: Structure-preserving and Topology-adaptive Deformation Network for 3D Reconstruction from a Single Image—0
Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information—0
VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation—0
Cascaded Classification Models: Combining Models for Holistic Scene Understanding—0
Cascade learning in multi-task encoder-decoder networks for concurrent bone segmentation and glenohumeral joint assessment in shoulder CT scans—0
CAT3D: Create Anything in 3D with Multi-View Diffusion Models—0
CBARF: Cascaded Bundle-Adjusting Neural Radiance Fields from Imperfect Camera Poses—0
CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction—0
CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting—0
CD^2: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance—0
Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by Global Optimization—0
CDI3D: Cross-guided Dense-view Interpolation for 3D Reconstruction—0
CD-NGP: A Fast Scalable Continual Representation for Dynamic Scenes—0
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction—0
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems—0
C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds—0
CharNeRF: 3D Character Generation from Concept Art—0
Alligat0R: Pre-Training Through Co-Visibility Segmentation for Relative Camera Pose Regression—0
CHROME: Clothed Human Reconstruction with Occlusion-Resilience and Multiview-Consistency from a Single Image—0
Stereo-based terrain traversability analysis using normal-based segmentation and superpixel surface analysis—0
Class Specific 3D Object Shape Priors Using Surface Normals—0
Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations—0
Clustering Assisted Fundamental Matrix Estimation—0
Stereo camera system calibration: the need of two sets of parameters—0
StereoDRNet: Dilated Residual Stereo Net—0
StereoDRNet: Dilated Residual StereoNet—0
Coded Illumination for 3D Lensless Imaging—0
CodeMapping: Real-Time Dense Mapping for Sparse SLAM using Compact Scene Representations—0
Coherence As Texture - Passive Textureless 3D Reconstruction by Self-interference—0
Coherent3D: Coherent 3D Portrait Video Reconstruction via Triplane Fusion—0
Coherent 3D Portrait Video Reconstruction via Triplane Fusion—0
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras—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