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 1451–1475 of 2326 papers

TitleStatusHype
SiLK: Simple Learned Keypoints—0
LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and ScenesCode0
What You Can Reconstruct From a Shadow—0
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action—0
PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction—0
Ponder: Point Cloud Pre-training via Neural Rendering—0
Equivariant Light Field Convolution and Transformer—0
Polarimetric Multi-View Inverse Rendering—0
Coded Illumination for 3D Lensless Imaging—0
PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields—0
UNIKD: UNcertainty-filtered Incremental Knowledge Distillation for Neural Implicit RepresentationCode0
Seafloor-Invariant Caustics Removal from Underwater Imagery—0
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach—0
Shared Coupling-bridge for Weakly Supervised Local Feature LearningCode0
SST: Real-time End-to-end Monocular 3D Reconstruction via Sparse Spatial-Temporal Guidance—0
Structured 3D Features for Reconstructing Controllable Avatars—0
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction—0
Real Time Incremental Image Mosaicking Without Use of Any Camera Parameter—0
Fast Non-Rigid Radiance Fields from Monocularized Data—0
Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary—0
SparseFusion: Distilling View-conditioned Diffusion for 3D Reconstruction—0
3D face reconstruction from single image with generative adversarial networks—0
SparsePose: Sparse-View Camera Pose Regression and Refinement—0
DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model—0
Adjustable Method Based on Body Parts for Improving the Accuracy of 3D Reconstruction in Visually Important Body Parts from Silhouettes—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