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

Showing 14761500 of 2326 papers

TitleStatusHype
Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms0
Representing 3D Shapes with Probabilistic Directed Distance Fields0
PERF: Performant, Explicit Radiance Fields0
Sparse Depth Completion with Semantic Mesh Deformation Optimization0
ScaleNet: A Shallow Architecture for Scale EstimationCode1
Adversarial Parametric Pose PriorCode1
What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction0
Input-level Inductive Biases for 3D Reconstruction0
Fast Organization of Objects' Spatial Positions in Manipulator Space from Single RGB-D CameraCode0
Generalized Binary Search Network for Highly-Efficient Multi-View StereoCode1
Geometry-aware Two-scale PIFu Representation for Human Reconstruction0
CoNeRF: Controllable Neural Radiance FieldsCode1
MonoScene: Monocular 3D Semantic Scene CompletionCode1
3DVNet: Multi-View Depth Prediction and Volumetric RefinementCode1
Generating Diverse 3D Reconstructions from a Single Occluded Face ImageCode1
VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and FusionCode1
3D Reconstruction Using a Linear Laser Scanner and a CameraCode1
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision0
Dense Keypoints via Multiview Supervision0
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies0
A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction NetworksCode1
ESL: Event-based Structured Light0
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions0
Urban Radiance Fields0
TransMVSNet: Global Context-aware Multi-view Stereo Network with TransformersCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
13D-R2N2Overall0.63Unverified
2GipumaOverall0.58Unverified
3COLMAPOverall0.53Unverified
4MVSNetOverall0.46Unverified
5Vis-MVSNetOverall0.37Unverified
6AA-RMVSNetOverall0.36Unverified
7Cas-MVSNetOverall0.36Unverified
8EPP-MVSNetOverall0.36Unverified
9PatchmatchNetOverall0.35Unverified
10CVP-MVSNetOverall0.35Unverified
#ModelMetricClaimedVerifiedStatus
1MD-GONIoU92.8Unverified
2POCOIoU92.6Unverified
3FS-SDFIoU91.2Unverified
4DP-ConvONetIoU89.5Unverified
5ConvONetIoU88.4Unverified
6ONetIoU76.1Unverified
7EVolTIoU73.8Unverified
8ZubicLioIoU65.43Unverified
#ModelMetricClaimedVerifiedStatus
1AttSets3DIoU0.64Unverified
2PSGN3DIoU0.64Unverified
3OGN3DIoU0.6Unverified
43D-R2N23DIoU0.56Unverified
#ModelMetricClaimedVerifiedStatus
1Scan2CADAverage Accuracy31.68Unverified
23DMatchAverage Accuracy10.29Unverified
#ModelMetricClaimedVerifiedStatus
1SVCPChamfer10Unverified
#ModelMetricClaimedVerifiedStatus
1EVLAccuracy18.2Unverified
#ModelMetricClaimedVerifiedStatus
1EVLAccuracy5.7Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)3DIoU89.4Unverified