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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 876–900 of 2326 papers

TitleStatusHype
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation—0
Free3D: Consistent Novel View Synthesis without 3D RepresentationCode1
RING-NeRF : Rethinking Inductive Biases for Versatile and Efficient Neural Fields—0
Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian ParticleCode1
Evaluating the point cloud of individual trees generated from images based on Neural Radiance fields (NeRF) method—0
Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages—0
Artificial Neural Network for Estimation of Physical Parameters of Sea Water using LiDAR Waveforms—0
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRICode1
ReconFusion: 3D Reconstruction with Diffusion Priors—0
R3D-SWIN:Use Shifted Window Attention for Single-View 3D Reconstruction—0
Prompt2NeRF-PIL: Fast NeRF Generation via Pretrained Implicit Latent—0
DreaMo: Articulated 3D Reconstruction From A Single Casual Video—0
iMatching: Imperative Correspondence LearningCode1
Light Field Imaging in the Restrictive Object Space based on Flexible Angular Plane—0
Steerers: A framework for rotation equivariant keypoint descriptorsCode1
Slice3D: Multi-Slice, Occlusion-Revealing, Single View 3D Reconstruction—0
RNb-NeuS: Reflectance and Normal-based Multi-View 3D ReconstructionCode1
Self-Evolving Neural Radiance FieldsCode1
NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance—0
UAVs and Birds: Enhancing Short-Range Navigation through Budgerigar Flight Studies—0
Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy—0
HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from VideoCode2
Dispersed Structured Light for Hyperspectral 3D Imaging—0
Portrait4D: Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data—0
Volumetric Cloud Field 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