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 526–550 of 2326 papers

TitleStatusHype
Pixel-Aligned Multi-View Generation with Depth Guided Decoder—0
TranSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Transformers—0
Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs—0
AdaOcc: Adaptive-Resolution Occupancy Prediction—0
Temporally-consistent 3D Reconstruction of Birds—0
S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D Control PointsCode1
Transientangelo: Few-Viewpoint Surface Reconstruction Using Single-Photon Lidar—0
Subsurface Scattering for 3D Gaussian Splatting—0
DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments—0
MeTTA: Single-View to 3D Textured Mesh Reconstruction with Test-Time Adaptation—0
PartGS:Learning Part-aware 3D Representations by Fusing 2D Gaussians and SuperquadricsCode2
MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model—0
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked AutoencodersCode0
SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views—0
Single-image coherent reconstruction of objects and humans—0
Comparative Evaluation of 3D Reconstruction Methods for Object Pose EstimationCode1
Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching NetworksCode0
NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild—0
PBIR-NIE: Glossy Object Capture under Non-Distant Lighting—0
HDRGS: High Dynamic Range Gaussian Splatting—0
3D Reconstruction of Protein Structures from Multi-view AFM Images using Neural Radiance Fields (NeRFs)—0
Mesh deformation-based single-view 3D reconstruction of thin eyeglasses frames with differentiable rendering—0
CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EMCode1
Visual SLAM with 3D Gaussian Primitives and Depth Priors Enabling Novel View Synthesis—0
AugGS: Self-augmented Gaussians with Structural Masks for Sparse-view 3D 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