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 501–510 of 2326 papers

Title	Date	Tasks	Status	Hype
Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications	Sep 11, 2024	3D ReconstructionSuper-Resolution	CodeCode Available	0
Single-View 3D Reconstruction via SO(2)-Equivariant Gaussian Sculpting Networks	Sep 11, 2024	3D Object Reconstruction3D Reconstruction	—Unverified	0
Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models	Sep 11, 2024	3D Generation3D Reconstruction	CodeCode Available	3
Sources of Uncertainty in 3D Scene Reconstruction	Sep 10, 2024	3D Reconstruction3D Scene Reconstruction	CodeCode Available	1
Online 3D reconstruction and dense tracking in endoscopic videos	Sep 9, 2024	3D Reconstruction3D Scene Reconstruction	CodeCode Available	1
Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering	Sep 9, 2024	3D ReconstructionInverse Rendering	—Unverified	0
CD-NGP: A Fast Scalable Continual Representation for Dynamic Scenes	Sep 8, 2024	3D ReconstructionContinual Learning	—Unverified	0
Ultron: Enabling Temporal Geometry Compression of 3D Mesh Sequences using Temporal Correspondence and Mesh Deformation	Sep 8, 2024	3D ReconstructionModel Compression	CodeCode Available	0
Optimizing 3D Gaussian Splatting for Sparse Viewpoint Scene Reconstruction	Sep 5, 2024	3DGS3D Reconstruction	—Unverified	0
LM-Gaussian: Boost Sparse-view 3D Gaussian Splatting with Large Model Priors	Sep 5, 2024	3DGS3D Reconstruction	—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