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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 304 papers

TitleStatusHype
Learnable Motion Coherence for Correspondence Pruning—0
Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares—0
Learning Multi-View Camera Relocalization With Graph Neural Networks—0
Learning Probabilistic Coordinate Fields for Robust Correspondences—0
Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images—0
Learning Rotation-Equivariant Features for Visual Correspondence—0
Learning to Switch CNNs with Model Agnostic Meta Learning for Fine Precision Visual Servoing—0
LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives—0
Level Set-Based Camera Pose Estimation From Multiple 2D/3D Ellipse-Ellipsoid Correspondences—0
Light3R-SfM: Towards Feed-forward Structure-from-Motion—0
LiM-Loc: Visual Localization with Dense and Accurate 3D Reference Maps Directly Corresponding 2D Keypoints to 3D LiDAR Point Clouds—0
Line-based Camera Pose Estimation in Point Cloud of Structured Environments—0
Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization—0
Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization—0
Long-term 3D Localization and Pose from Semantic Labellings—0
MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR—0
Mono3R: Exploiting Monocular Cues for Geometric 3D Reconstruction—0
Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras—0
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Pose—0
Large Scale Joint Semantic Re-Localisation and Scene Understanding via Globally Unique Instance Coordinate Regression—0
RUBIK: A Structured Benchmark for Image Matching across Geometric Challenges—0
S3LAM: Structured Scene SLAM—0
Sampling Based Scene-Space Video Processing—0
Scalable Structure From Motion for Densely Sampled Videos—0
Scaling 4D Representations—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