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 276–300 of 304 papers

TitleStatusHype
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?—0
Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization—0
Refractive Structure-From-Motion Through a Flat Refractive Interface—0
Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence—0
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution from a Blurred Image Sequence—0
Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization—0
Long-term 3D Localization and Pose from Semantic Labellings—0
Benchmarking 6DOF Outdoor Visual Localization in Changing ConditionsCode0
Discriminative Optimization: Theory and Applications to Computer Vision Problems—0
Unsupervised Learning of Depth and Ego-Motion from VideoCode0
Quaternion Based Camera Pose Estimation From Matched Feature Points—0
Relative Camera Pose Estimation Using Convolutional Neural NetworksCode0
Computing Egomotion with Local Loop Closures for Egocentric Videos—0
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground TruthCode0
Absolute Pose Estimation from Line Correspondences using Direct Linear Transformation—0
Camera Pose Estimation from Lines using Plücker Coordinates—0
Single Image Camera Calibration With Lenticular Arrays for Augmented Reality—0
SHAPE: Linear-Time Camera Pose Estimation With Quadratic Error-Decay—0
Towards the Application of Linear Programming Methods For Multi-Camera Pose Estimation—0
Camera Pose Voting for Large-Scale Image-Based Localization—0
Depth Extraction from Videos Using Geometric Context and Occlusion Boundaries—0
P3.5P: Pose Estimation With Unknown Focal Length—0
Depth Camera Tracking With Contour Cues—0
Scalable Structure From Motion for Densely Sampled Videos—0
Direct Structure Estimation for 3D Reconstruction—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