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 1401–1450 of 2326 papers

TitleStatusHype
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis—0
MonoTrack: Shuttle trajectory reconstruction from monocular badminton video—0
BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume FusionCode1
Online panoptic 3D reconstruction as a Linear Assignment ProblemCode1
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence—0
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions—0
RFNet-4D++: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds with Cross-Attention Spatio-Temporal FeaturesCode1
3D Shape Reconstruction from 2D Images with Disentangled Attribute FlowCode1
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian—0
Towards Learning Neural Representations from Shadows—0
DeepShadow: Neural Shape from ShadowCode1
RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformers—0
A Real World Dataset for Multi-view 3D Reconstruction—0
φ-SfT: Shape-from-Template with a Physics-Based Deformation ModelCode1
PlaneMVS: 3D Plane Reconstruction from Multi-View StereoCode1
NeRFusion: Fusing Radiance Fields for Large-Scale Scene ReconstructionCode2
DiFT: Differentiable Differential Feature Transform for Multi-View Stereo—0
OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D ReconstructionCode2
VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single and Multi-view 3D Reconstruction—0
Multi-sensor large-scale dataset for multi-view 3D reconstructionCode1
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers—0
Human-Aware Object Placement for Visual Environment ReconstructionCode1
Highly Accurate Dichotomous Image SegmentationCode4
Addressing the Shape-Radiance Ambiguity in View-Dependent Radiance Fields—0
Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D ReconstructionCode1
CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size EstimationCode2
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
Sketched RT3D: How to reconstruct billions of photons per secondCode0
Object Pose Estimation using Mid-level Visual RepresentationsCode0
H4D: Human 4D Modeling by Learning Neural Compositional Representation—0
ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse views—0
ERF: Explicit Radiance Field Reconstruction From Scratch—0
Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function—0
RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training—0
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression—0
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints—0
MITI: SLAM Benchmark for Laparoscopic Surgery—0
NeuVV: Neural Volumetric Videos with Immersive Rendering and Editing—0
NeAT: Neural Adaptive TomographyCode1
HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly implicitsCode1
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization—0
Light field Rectification based on relative pose estimation—0
Unsupervised Single-shot Depth Estimation using Perceptual ReconstructionCode0
On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad GenerationsCode0
Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform—0
Unsupervised Severely Deformed Mesh Reconstruction (DMR) from a Single-View Image—0
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data—0
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination SystemsCode1
Point-NeRF: Point-based Neural Radiance FieldsCode3
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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