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 776–800 of 2326 papers

TitleStatusHype
DARB-Splatting: Generalizing Splatting with Decaying Anisotropic Radial Basis Functions—0
Continuous 3D Perception Model with Persistent State—0
A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography—0
Survey on Monocular Metric Depth Estimation—0
Procedural Generation of 3D Maize Plant Architecture from LIDAR Data—0
Linea alba 3D morphometric variability by CT scan exploration—0
RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering—0
UVRM: A Scalable 3D Reconstruction Model from Unposed Videos—0
Poxel: Voxel Reconstruction for 3D Printing—0
Unified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures—0
Scalable and High-Quality Neural Implicit Representation for 3D Reconstruction—0
Representation Learning of Point Cloud Upsampling in Global and Local Inputs—0
SplatMAP: Online Dense Monocular SLAM with 3D Gaussian Splatting—0
A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision—0
AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scene—0
AR4D: Autoregressive 4D Generation from Monocular Videos—0
Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery—0
LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields—0
MAGE : Single Image to Material-Aware 3D via the Multi-View G-Buffer Estimation Model—0
Gromov-Wasserstein Problem with Cyclic Symmetry—0
AniGrad: Anisotropic Gradient-Adaptive Sampling for 3D Reconstruction From Monocular Video—0
Vision-Language Embodiment for Monocular Depth Estimation—0
HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation—0
Shading Meets Motion: Self-supervised Indoor 3D Reconstruction Via Simultaneous Shape-from-Shading and Structure-from-Motion—0
PRaDA: Projective Radial Distortion Averaging—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