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 701–725 of 2326 papers

TitleStatusHype
EventEgo3D: 3D Human Motion Capture from Egocentric Event StreamsCode1
GoMVS: Geometrically Consistent Cost Aggregation for Multi-View StereoCode2
MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views—0
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
3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level SupervisionsCode2
Joint Reconstruction of 3D Human and Object via Contact-Based Refinement TransformerCode2
OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point CloudsCode2
RaFE: Generative Radiance Fields Restoration—0
The More You See in 2D, the More You Perceive in 3DCode2
WorDepth: Variational Language Prior for Monocular Depth EstimationCode1
Gen3DSR: Generalizable 3D Scene Reconstruction via Divide and Conquer from a Single ViewCode2
APC2Mesh: Bridging the gap from occluded building façades to full 3D models—0
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR SynthesisCode3
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
Neural Radiance Fields with Torch Units—0
Unsupervised Occupancy Learning from Sparse Point CloudCode1
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
InstantSplat: Sparse-view SfM-free Gaussian Splatting in SecondsCode5
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and DenoisingCode0
Total-Decom: Decomposed 3D Scene Reconstruction with Minimal InteractionCode2
Neural Fields for 3D Tracking of Anatomy and Surgical Instruments in Monocular Laparoscopic Video Clips—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