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

Showing 351–375 of 2326 papers

TitleStatusHype
MobileBrick: Building LEGO for 3D Reconstruction on Mobile DevicesCode1
Efficient Implicit Neural Reconstruction Using LiDARCode1
UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D ReconstructionCode1
SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous DrivingCode1
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking DatasetsCode1
UAVStereo: A Multiple Resolution Dataset for Stereo Matching in UAV ScenariosCode1
Mono-STAR: Mono-camera Scene-level Tracking and ReconstructionCode1
Self-Supervised Super-Plane for Neural 3D ReconstructionCode1
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same ActionCode1
EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual QueriesCode1
Fast and Lightweight Scene Regressor for Camera RelocalizationCode1
MaRF: Representing Mars as Neural Radiance FieldsCode1
NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D ReconstructionCode1
One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume DistillationCode1
PatchMatch-Stereo-Panorama, a fast dense reconstruction from 360° video imagesCode1
Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual RepresentationCode1
Multi-task Learning for Camera CalibrationCode1
Level-S^2fM: Structure from Motion on Neural Level Set of Implicit SurfacesCode1
DP-NeRF: Deblurred Neural Radiance Field with Physical Scene PriorsCode1
Detecting Line Segments in Motion-blurred Images with EventsCode1
Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and TrackingCode1
Multi-View Guided Multi-View StereoCode1
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel LogisticsCode1
Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel SupervisionsCode1
3D GAN Inversion with Pose OptimizationCode1
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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