SOTAVerified

Camera Pose Estimation

Camera pose estimation is a crucial task in computer vision and robotics that involves determining the position and orientation (pose) of a camera relative to a given reference frame. This task is essential for various applications, such as augmented reality, 3D reconstruction, SLAM, and autonomous navigation.

Papers

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Showing 101–125 of 304 papers

TitleStatusHype
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence—0
Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison—0
GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation—0
ADen: Adaptive Density Representations for Sparse-view Camera Pose Estimation—0
KeyGS: A Keyframe-Centric Gaussian Splatting Method for Monocular Image Sequences—0
Large-scale visual SLAM for in-the-wild videos—0
Learnable Motion Coherence for Correspondence Pruning—0
GCRayDiffusion: Pose-Free Surface Reconstruction via Geometric Consistent Ray Diffusion—0
GaussianCAD: Robust Self-Supervised CAD Reconstruction from Three Orthographic Views Using 3D Gaussian Splatting—0
Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty—0
CoVisPose: Co-Visibility Pose Transformer for Wide-Baseline Relative Pose Estimation in 360◦ Indoor Panoramas—0
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow—0
FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views—0
Correspondences of the Third Kind: Camera Pose Estimation from Object Reflection—0
Five Shades of Grey for Fast and Reliable Camera Pose Estimation—0
Graph-CoVis: GNN-based Multi-view Panorama Global Pose Estimation—0
Correspondence-Guided SfM-Free 3D Gaussian Splatting for NVS—0
E^3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images—0
Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction—0
Introduction to Camera Pose Estimation with Deep Learning—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
FAR: Flexible Accurate and Robust 6DoF Relative Camera Pose Estimation—0
Hybrid Camera Pose Estimation—0
FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Monodepth2Average Translational Error et[%]43.21—Unverified
2SfMLearnerAverage Translational Error et[%]29.78—Unverified
3GeoNetAverage Translational Error et[%]26.31—Unverified
4SC-DepthAverage Translational Error et[%]12.2—Unverified
5DeepMatchVOAverage Translational Error et[%]11.05—Unverified
6SCIPaDAverage Translational Error et[%]8.63—Unverified
7Manydepth2Average Translational Error et[%]7.15—Unverified