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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 941–950 of 2326 papers

Title	Date	Tasks	Status	Hype
Open-NeRF: Towards Open Vocabulary NeRF Decomposition	Oct 25, 2023	3D ReconstructionNeRF	—Unverified	0
Interaction-Driven Active 3D Reconstruction with Object Interiors	Oct 23, 2023	3D ReconstructionActive 3D Reconstruction	—Unverified	0
A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video	Oct 22, 2023	3D ReconstructionAnatomy	—Unverified	0
UE4-NeRF:Neural Radiance Field for Real-Time Rendering of Large-Scale Scene	Oct 20, 2023	3D Reconstruction4k	—Unverified	0
Single-view 3D reconstruction via inverse procedural modeling	Oct 20, 2023	3D ReconstructionSingle-View 3D Reconstruction	—Unverified	0
ShapeGraFormer: GraFormer-Based Network for Hand-Object Reconstruction from a Single Depth Map	Oct 18, 2023	3D ReconstructionObject	—Unverified	0
Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priors	Oct 17, 2023	3D ReconstructionSimultaneous Localization and Mapping	—Unverified	0
Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model	Oct 16, 2023	3D ReconstructionNeRF	—Unverified	0
Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker	Oct 16, 2023	3D Reconstruction	CodeCode Available	1
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior	Oct 15, 2023	3D ReconstructionDepth Completion	—Unverified	0

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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