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 1201–1225 of 2326 papers

TitleStatusHype
GES : Generalized Exponential Splatting for Efficient Radiance Field Rendering—0
QUADify: Extracting Meshes with Pixel-level Details and Materials from Images—0
L0-Sampler: An L0 Model Guided Volume Sampling for NeRF—0
Sparse Views Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo—0
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular VideoCode0
DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans—0
3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces—0
Coherence As Texture - Passive Textureless 3D Reconstruction by Self-interference—0
Slice3D: Multi-Slice Occlusion-Revealing Single View 3D Reconstruction—0
Informative Rays Selection for Few-Shot Neural Radiance Fields—0
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation—0
In-Hand 3D Object Reconstruction from a Monocular RGB Video—0
Objects as volumes: A stochastic geometry view of opaque solids—0
SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition—0
Scalable 3D Reconstruction From Single Particle X-Ray Diffraction Images Based on Online Machine Learning—0
Anatomical basis of sex differences in the electrocardiogram identified by three-dimensional torso-heart imaging reconstruction pipeline—0
SyncDreamer for 3D Reconstruction of Endangered Animal Species with NeRF and NeuS—0
PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar—0
Visual Tomography: Physically Faithful Volumetric Models of Partially Translucent Objects—0
UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections—0
EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping—0
Primitive-based 3D Human-Object Interaction Modelling and Programming—0
Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction—0
GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning—0
Scene 3-D Reconstruction System in Scattering Medium—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