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 301–325 of 2326 papers

TitleStatusHype
Coordinate Quantized Neural Implicit Representations for Multi-view ReconstructionCode1
DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume ReconstructionCode1
O^2-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion ModelCode1
Long-Range Grouping Transformer for Multi-View 3D ReconstructionCode1
ObjectSDF++: Improved Object-Compositional Neural Implicit SurfacesCode1
A One Stop 3D Target Reconstruction and multilevel Segmentation MethodCode1
PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape ProgramsCode1
Reconstructing Three-Dimensional Models of Interacting HumansCode1
Creative Birds: Self-Supervised Single-View 3D Style TransferCode1
Replay: Multi-modal Multi-view Acted Videos for Casual HolographyCode1
SimCol3D -- 3D Reconstruction during Colonoscopy ChallengeCode1
NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDFCode1
NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth EstimationCode1
NEAT: Distilling 3D Wireframes from Neural Attraction FieldsCode1
Symphonize 3D Semantic Scene Completion with Contextual Instance QueriesCode1
Generative Proxemics: A Prior for 3D Social Interaction from ImagesCode1
Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D DataCode1
SMART: Spatial Modeling Algorithms for Reaction and TransportCode1
Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance FieldsCode1
DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene GenerationCode1
BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single ImageCode1
Cross3DVG: Cross-Dataset 3D Visual Grounding on Different RGB-D ScansCode1
Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic ModelsCode1
Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance FieldsCode1
ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View SynthesisCode1
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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