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 326–350 of 2326 papers

TitleStatusHype
Facial Surgery Preview Based on the Orthognathic Treatment Prediction—0
Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile RobotsCode1
VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping—0
AURORA: Automated Unleash of 3D Room Outlines for VR Applications—0
DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian SplattingCode1
PBR-NeRF: Inverse Rendering with Physics-Based Neural FieldsCode1
MAC-Ego3D: Multi-Agent Gaussian Consensus for Real-Time Collaborative Ego-Motion and Photorealistic 3D ReconstructionCode2
Mixture of neural fields for heterogeneous reconstruction in cryo-EM—0
Hyperbolic-constraint Point Cloud Reconstruction from Single RGB-D Images—0
LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors—0
A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data—0
FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction—0
SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB VideosCode5
Learning Camera Movement Control from Real-World Drone Videos—0
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models—0
Coherent3D: Coherent 3D Portrait Video Reconstruction via Triplane Fusion—0
3D Mesh Editing using Masked LRMs—0
Pragmatist: Multiview Conditional Diffusion Models for High-Fidelity 3D Reconstruction from Unposed Sparse Views—0
RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation—0
LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation Models—0
From an Image to a Scene: Learning to Imagine the World from a Million 360 VideosCode2
Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction—0
Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features—0
Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories—0
Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene ReconstructionCode2
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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