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 951–975 of 2326 papers

TitleStatusHype
SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot—0
GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians—0
SurgPointTransformer: Vertebrae Shape Completion with RGB-D Data—0
An Illumination-Robust Feature Extractor Augmented by Relightable 3D Reconstruction—0
Revisiting the Role of Texture in 3D Person Re-identification—0
Flex3D: Feed-Forward 3D Generation with Flexible Reconstruction Model and Input View Curation—0
Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance—0
3DPX: Single Panoramic X-ray Analysis Guided by 3D Oral Structure Reconstruction—0
TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic SceneCode0
Towards Unified 3D Hair Reconstruction from Single-View Portraits—0
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth EstimationCode0
Frequency-based View Selection in Gaussian Splatting Reconstruction—0
Underground Mapping and Localization Based on Ground-Penetrating Radar—0
SpikeGS: Learning 3D Gaussian Fields from Continuous Spike StreamCode0
AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmark—0
ReLoo: Reconstructing Humans Dressed in Loose Garments from Monocular Video in the Wild—0
ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events—0
MGSO: Monocular Real-time Photometric SLAM with Efficient 3D Gaussian Splatting—0
HGSLoc: 3DGS-based Heuristic Camera Pose Refinement—0
MinD-3D++: Advancing fMRI-Based 3D Reconstruction with High-Quality Textured Mesh Generation and a Comprehensive Dataset—0
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion—0
CSS: Overcoming Pose and Scene Challenges in Crowd-Sourced 3D Gaussian Splatting—0
VI3DRM:Towards meticulous 3D Reconstruction from Sparse Views via Photo-Realistic Novel View Synthesis—0
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy—0
SPARK: Self-supervised Personalized Real-time Monocular Face Capture—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