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 276–300 of 304 papers

TitleStatusHype
Perspective Plane Program Induction from a Single Image—0
PixSelect: Less but Reliable Pixels for Accurate and Efficient Localization—0
Pose Estimation and Tracking for ASIST—0
Pose-GNN : Camera Pose Estimation System Using Graph Neural Networks—0
PosePilot: Steering Camera Pose for Generative World Models with Self-supervised Depth—0
Position Estimation of Camera Based on Unsupervised Learning—0
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation—0
PRIF: Primary Ray-based Implicit Function—0
Privacy Preserving Image-Based Localization—0
Privacy Preserving Image Queries for Camera Localization—0
Privacy Preserving Structure-from-Motion—0
Quaternion Based Camera Pose Estimation From Matched Feature Points—0
RADA: Robust and Accurate Feature Learning with Domain Adaptation—0
RA-NeRF: Robust Neural Radiance Field Reconstruction with Accurate Camera Pose Estimation under Complex Trajectories—0
Real-Time Camera Pose Estimation for Sports Fields—0
Reassessing the Limitations of CNN Methods for Camera Pose Regression—0
Refractive Structure-From-Motion Through a Flat Refractive Interface—0
Regist3R: Incremental Registration with Stereo Foundation Model—0
RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training—0
RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos—0
RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes—0
Robot Hand-Eye Calibration using Structure-from-Motion—0
Robustifying the Multi-Scale Representation of Neural Radiance Fields—0
Rolling Shutter and Radial Distortion Are Features for High Frame Rate Multi-Camera Tracking—0
RUBIK: A Structured Benchmark for Image Matching across Geometric Challenges—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