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 921–930 of 2326 papers

Title	Date	Tasks	Status
EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction	Nov 16, 2023	3D ReconstructionGPU	—Unverified
DSR-Diff: Depth Map Super-Resolution with Diffusion Model	Nov 16, 2023	3D ReconstructionDepth Map Super-Resolution	CodeCode Available
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid	Nov 14, 2023	3D ReconstructionSurface Reconstruction	—Unverified
L_0-Sampler: An L_0 Model Guided Volume Sampling for NeRF	Nov 13, 2023	3D ReconstructionNeRF	—Unverified
3DFusion, A real-time 3D object reconstruction pipeline based on streamed instance segmented data	Nov 11, 2023	3D Object Reconstruction3D Reconstruction	—Unverified
Automatic extraction and 3D reconstruction of split wire from point cloud data based on improved DPC algorithm	Nov 10, 2023	3D Reconstruction	—Unverified
ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image	Nov 9, 2023	3D Generation3D Reconstruction	—Unverified
High-fidelity 3D Reconstruction of Plants using Neural Radiance Field	Nov 7, 2023	3D ReconstructionNeRF	—Unverified
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding	Nov 7, 2023	3D ReconstructionBenchmarking	CodeCode Available
Fast Point Cloud to Mesh Reconstruction for Deformable Object Tracking	Nov 5, 2023	3D Reconstruction4D reconstruction	—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