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 2051–2100 of 2326 papers

TitleStatusHype
DeepMesh: Differentiable Iso-Surface Extraction—0
DeepMLE: A Robust Deep Maximum Likelihood Estimator for Two-view Structure from Motion—0
Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspective—0
DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar—0
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method—0
SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization—0
Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene—0
Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image—0
Deep Textured 3D Reconstruction of Human Bodies—0
SuperPrimitive: Scene Reconstruction at a Primitive Level—0
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry—0
Volumetric Cloud Field Reconstruction—0
Defogging Kinect: Simultaneous Estimation of Object Region and Depth in Foggy Scenes—0
AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmark—0
Surface-Aware Distilled 3D Semantic Features—0
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image—0
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields—0
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image—0
Deltille Grids for Geometric Camera Calibration—0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs—0
Denoising Diffusion via Image-Based Rendering—0
De-noising, Stabilizing and Completing 3D Reconstructions On-the-go using Plane Priors—0
Dense 3D Reconstruction for Visual Tunnel Inspection using Unmanned Aerial Vehicle—0
Dense 3D Reconstruction from Severely Blurred Images Using a Single Moving Camera—0
Dense 3D Reconstruction Through Lidar: A Comparative Study on Ex-vivo Porcine Tissue—0
Dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture—0
Dense Keypoints via Multiview Supervision—0
Dense Matchers for Dense Tracking—0
Dense Multi-view 3D-reconstruction Without Dense Correspondences—0
Dense Reconstruction Using 3D Object Shape Priors—0
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension—0
Dense RGB-D-Inertial SLAM with Map Deformations—0
Dense RGB-D semantic mapping with Pixel-Voxel neural network—0
Dense-SfM: Structure from Motion with Dense Consistent Matching—0
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video—0
Dense Voxel 3D Reconstruction Using a Monocular Event Camera—0
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience—0
Surface HOF: Surface Reconstruction from a Single Image Using Higher Order Function Networks—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
Depth Priors in Removal Neural Radiance Fields—0
DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments—0
Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction—0
DeSplat: Decomposed Gaussian Splatting for Distractor-Free Rendering—0
Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction—0
X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models—0
Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling—0
DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction—0
DG-Recon: Depth-Guided Neural 3D Scene Reconstruction—0
Dietary Intake Estimation via Continuous 3D Reconstruction of Food—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