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 681–690 of 2326 papers

Title	Date	Tasks	Status	Score
LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstruction	Jul 23, 2023	3D ReconstructionObject	CodeCode Available	5
DreamSat: Towards a General 3D Model for Novel View Synthesis of Space Objects	Oct 7, 2024	3D ReconstructionNovel View Synthesis	CodeCode Available	5
Massively Parallel Multiview Stereopsis by Surface Normal Diffusion	Dec 1, 2015	3D ReconstructionGPU	CodeCode Available	5
MPF-GAN: an enhanced architecture for 3D face reconstruction	Oct 10, 2024	3D Face Reconstruction3D Reconstruction	CodeCode Available	5
Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction	Jul 11, 2023	3D ReconstructionDataset Generation	CodeCode Available	5
A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions	Jul 1, 2024	3D ReconstructionSurface Reconstruction	CodeCode Available	5
Learning to Generate Dense Point Clouds with Textures on Multiple Categories	Dec 22, 2019	3D Reconstruction	CodeCode Available	5
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction	Dec 1, 2019	2D Pose Estimation3D Pose Estimation	CodeCode Available	5
Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person	Jan 6, 2018	3D ReconstructionClustering	CodeCode Available	5
Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement	May 25, 2023	3D ReconstructionDisentanglement	CodeCode Available	5

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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