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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1376–1400 of 2326 papers

TitleStatusHype
3D Reconstruction of Objects in Hands without Real World 3D Supervision—0
UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation—0
Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies—0
Second-order Anisotropic Gaussian Directional Derivative Filters for Blob Detection—0
NSLF-OL: Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction—0
Learning Articulated Shape with Keypoint Pseudo-labels from Web Images—0
Combining HoloLens with Instant-NeRFs: Advanced Real-Time 3D Mobile Mapping—0
Multi-View Stereo Representation Revisit: Region-Aware MVSNet—0
Shape-Net: Room Layout Estimation from Panoramic Images Robust to Occlusion using Knowledge Distillation with 3D Shapes as Additional Inputs—0
NoiseTrans: Point Cloud Denoising with Transformers—0
Unsupervised Style-based Explicit 3D Face Reconstruction from Single Image—0
Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting—0
A Comparative Neural Radiance Field (NeRF) 3D Analysis of Camera Poses from HoloLens Trajectories and Structure from Motion—0
3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset—0
Single-View View Synthesis with Self-Rectified Pseudo-Stereo—0
SurfelNeRF: Neural Surfel Radiance Fields for Online Photorealistic Reconstruction of Indoor Scenes—0
EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual HullsCode0
Neural Lens Modeling—0
NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic RadiographsCode0
3D GANs and Latent Space: A comprehensive survey—0
Photometric Correction for Infrared Sensors—0
Analysis of Sampling Strategies for Implicit 3D Reconstruction—0
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields—0
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction—0
Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes—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