SOTAVerified

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 551–575 of 2326 papers

TitleStatusHype
A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic SurgeryCode1
Opening the Black Box of 3D Reconstruction Error Analysis with VECTORCode1
LumiGauss: Relightable Gaussian Splatting in the WildCode2
Efficient NeRF Optimization -- Not All Samples Remain Equally Hard—0
BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical ApplicationsCode1
PanicleNeRF: low-cost, high-precision in-field phenotypingof rice panicles with smartphone—0
Enhanced Knee Kinematics: Leveraging Deep Learning and Morphing Algorithms for 3D Implant Modeling—0
Embodiment: Self-Supervised Depth Estimation Based on Camera Models—0
Structure from Motion-based Motion Estimation and 3D Reconstruction of Unknown Shaped Space Debris—0
IG-SLAM: Instant Gaussian SLAM—0
UlRe-NeRF: 3D Ultrasound Imaging through Neural Rendering with Ultrasound Reflection Direction Parameterization—0
X-ray nano-holotomography reconstruction with simultaneous probe retrieval—0
SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos—0
Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle—0
A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific FittingCode0
IOVS4NeRF:Incremental Optimal View Selection for Large-Scale NeRFs—0
Floating No More: Object-Ground Reconstruction from a Single Image—0
UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation—0
Enhancement of 3D Gaussian Splatting using Raw Mesh for Photorealistic Recreation of Architectures—0
VoxDepth: Rectification of Depth Images on Edge Devices—0
3D Reconstruction of the Human Colon from Capsule Endoscope Video—0
Reconstruction of the shape of irregular rough particles from their interferometric images using a convolutional neural network—0
SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric LinearizationCode1
DirectL: Efficient Radiance Fields Rendering for 3D Light Field Displays—0
I Know About "Up"! Enhancing Spatial Reasoning in Visual Language Models Through 3D Reconstruction—0
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All datasetsDTUShapeNetData3D−R2N2Scan2CAD3DPeopleAria Digital Twin DatasetAria Synthetic EnvironmentsScanNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
13D-R2N2Overall0.63—Unverified
2GipumaOverall0.58—Unverified
3COLMAPOverall0.53—Unverified
4MVSNetOverall0.46—Unverified
5Vis-MVSNetOverall0.37—Unverified
6AA-RMVSNetOverall0.36—Unverified
7Cas-MVSNetOverall0.36—Unverified
8EPP-MVSNetOverall0.36—Unverified
9PatchmatchNetOverall0.35—Unverified
10CVP-MVSNetOverall0.35—Unverified
#ModelMetricClaimedVerifiedStatus
1MD-GONIoU92.8—Unverified
2POCOIoU92.6—Unverified
3FS-SDFIoU91.2—Unverified
4DP-ConvONetIoU89.5—Unverified
5ConvONetIoU88.4—Unverified
6ONetIoU76.1—Unverified
7EVolTIoU73.8—Unverified
8ZubicLioIoU65.43—Unverified
#ModelMetricClaimedVerifiedStatus
1AttSets3DIoU0.64—Unverified
2PSGN3DIoU0.64—Unverified
3OGN3DIoU0.6—Unverified
43D-R2N23DIoU0.56—Unverified
#ModelMetricClaimedVerifiedStatus
1Scan2CADAverage Accuracy31.68—Unverified
23DMatchAverage Accuracy10.29—Unverified
#ModelMetricClaimedVerifiedStatus
1SVCPChamfer10—Unverified
#ModelMetricClaimedVerifiedStatus
1EVLAccuracy18.2—Unverified
#ModelMetricClaimedVerifiedStatus
1EVLAccuracy5.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)3DIoU89.4—Unverified