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 601–610 of 2326 papers

Title	Date	Tasks	Status	Score
Context-Aware Outlier Rejection for Robust Multi-View 3D Tracking of Similar Small Birds in An Outdoor Aviary	Dec 21, 2024	3D Reconstruction	CodeCode Available	5
On Learning 3D Face Morphable Model from In-the-wild Images	Aug 28, 2018	3D ReconstructionFace Alignment	CodeCode Available	5
One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment	Dec 12, 2019	3D ReconstructionAll	CodeCode Available	5
Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields	Mar 17, 2024	3D ReconstructionNeRF	CodeCode Available	5
Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs	Mar 28, 2017	3D ReconstructionDecoder	CodeCode Available	5
OctNetFusion: Learning Depth Fusion from Data	Apr 4, 2017	3D Reconstruction	CodeCode Available	5
OMEGAS: Object Mesh Extraction from Large Scenes Guided by Gaussian Segmentation	Apr 24, 2024	3D ReconstructionObject	CodeCode Available	5
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders	Aug 19, 2024	3D Classification3D Reconstruction	CodeCode Available	5
Object Pose Estimation using Mid-level Visual Representations	Mar 2, 2022	3D ReconstructionObject	CodeCode Available	5
No-reference geometry quality assessment for colorless point clouds via list-wise rank learning	Feb 17, 2025	3D ReconstructionPoint Cloud Quality Assessment	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