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 201–225 of 304 papers

TitleStatusHype
Sampling Based Scene-Space Video Processing—0
Progressive Correspondence Pruning by Consensus LearningCode1
VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers—0
PoGO-Net: Pose Graph Optimization With Graph Neural NetworksCode1
Towards Accurate Active Camera LocalizationCode1
Learnable Motion Coherence for Correspondence Pruning—0
Benchmarking Image Retrieval for Visual LocalizationCode1
DeepMorph: A System for Hiding Bitstrings in Morphable Vector Drawings—0
P1AC: Revisiting Absolute Pose From a Single Affine CorrespondenceCode1
Can You Trust Your Pose? Confidence Estimation in Visual Localization—0
CaSPR: Learning Canonical Spatiotemporal Point Cloud RepresentationsCode1
Privacy Preserving Structure-from-Motion—0
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching—0
Event-based Stereo Visual OdometryCode1
Deep Keypoint-Based Camera Pose Estimation with Geometric ConstraintsCode1
Object-Centric Multi-View Aggregation—0
Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing—0
Perspective Plane Program Induction from a Single Image—0
Adversarial Transfer of Pose Estimation Regression—0
Height and Uprightness Invariance for 3D Prediction From a Single ViewCode1
Why Having 10,000 Parameters in Your Camera Model Is Better Than TwelveCode1
Learning Multi-View Camera Relocalization With Graph Neural Networks—0
End-to-End Camera Calibration for Broadcast Videos—0
TRPLP - Trifocal Relative Pose From Lines at Points—0
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB VideoCode1
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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