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 1126–1150 of 2326 papers

TitleStatusHype
Binomial Self-compensation for Motion Error in Dynamic 3D ScanningCode0
3D-COCO: extension of MS-COCO dataset for image detection and 3D reconstruction modules—0
Learning Topology Uniformed Face Mesh by Volume Rendering for Multi-view Reconstruction—0
RaFE: Generative Radiance Fields Restoration—0
APC2Mesh: Bridging the gap from occluded building façades to full 3D models—0
Neural Radiance Fields with Torch Units—0
TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for Autonomous Driving—0
Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion—0
Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion featuresCode0
3DGSR: Implicit Surface Reconstruction with 3D Gaussian SplattingCode0
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and DenoisingCode0
Neural Fields for 3D Tracking of Anatomy and Surgical Instruments in Monocular Laparoscopic Video Clips—0
WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion—0
SplatFace: Gaussian Splat Face Reconstruction Leveraging an Optimizable Surface—0
Spike-NeRF: Neural Radiance Field Based On Spike Camera—0
Creating a Digital Twin of Spinal Surgery: A Proof of Concept—0
Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion—0
latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction—0
Interactive Manipulation and Visualization of 3D Brain MRI for Surgical Training—0
Recent Trends in 3D Reconstruction of General Non-Rigid Scenes—0
FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videos—0
Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information—0
Hyperspectral Neural Radiance Fields—0
Isotropic Gaussian Splatting for Real-Time Radiance Field Rendering—0
T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image—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