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 1511–1520 of 2326 papers

Title	Date	Tasks	Status
Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision	Sep 20, 2022	3D ReconstructionAutonomous Vehicles	—Unverified
BuFF: Burst Feature Finder for Light-Constrained 3D Reconstruction	Sep 20, 2022	3D Reconstruction	—Unverified
Joint Reconstruction and Parcellation of Cortical Surfaces	Sep 19, 2022	3D ReconstructionGraph Classification	—Unverified
Neural Implicit Surface Reconstruction using Imaging Sonar	Sep 17, 2022	3D ReconstructionSurface Reconstruction	—Unverified
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields	Sep 17, 2022	3D geometry3D Reconstruction	—Unverified
INV-Flow2PoseNet: Light-Resistant Rigid Object Pose from Optical Flow of RGB-D Images using Images, Normals and Vertices	Sep 14, 2022	3D ReconstructionOptical Flow Estimation	—Unverified
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volumes	Sep 14, 2022	3D ReconstructionMulti-View 3D Reconstruction	—Unverified
Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions	Sep 7, 2022	3D Reconstruction	—Unverified
Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings	Sep 6, 2022	3D Reconstruction	—Unverified
3D Single-pixel imaging with active sampling patterns and learning based reconstruction	Sep 6, 2022	3D ReconstructionDeep Learning	—Unverified

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All datasets DTU ShapeNet Data3D−R2N2 Scan2CAD 3DPeople Aria Digital Twin Dataset Aria Synthetic Environments ScanNet

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	3D-R2N2	Overall	0.63	—	Unverified
2	Gipuma	Overall	0.58	—	Unverified
3	COLMAP	Overall	0.53	—	Unverified
4	MVSNet	Overall	0.46	—	Unverified
5	Vis-MVSNet	Overall	0.37	—	Unverified
6	AA-RMVSNet	Overall	0.36	—	Unverified
7	Cas-MVSNet	Overall	0.36	—	Unverified
8	EPP-MVSNet	Overall	0.36	—	Unverified
9	PatchmatchNet	Overall	0.35	—	Unverified
10	CVP-MVSNet	Overall	0.35	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MD-GON	IoU	92.8	—	Unverified
2	POCO	IoU	92.6	—	Unverified
3	FS-SDF	IoU	91.2	—	Unverified
4	DP-ConvONet	IoU	89.5	—	Unverified
5	ConvONet	IoU	88.4	—	Unverified
6	ONet	IoU	76.1	—	Unverified
7	EVolT	IoU	73.8	—	Unverified
8	ZubicLio	IoU	65.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AttSets	3DIoU	0.64	—	Unverified
2	PSGN	3DIoU	0.64	—	Unverified
3	OGN	3DIoU	0.6	—	Unverified
4	3D-R2N2	3DIoU	0.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Scan2CAD	Average Accuracy	31.68	—	Unverified
2	3DMatch	Average Accuracy	10.29	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SVCP	Chamfer	10	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EVL	Accuracy	18.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EVL	Accuracy	5.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Atlas (finetuned)	3DIoU	89.4	—	Unverified