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 26–50 of 304 papers

TitleStatusHype
PVO: Panoptic Visual OdometryCode2
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the WildCode2
Recollection from Pensieve: Novel View Synthesis via Learning from Uncalibrated VideosCode2
MeshLoc: Mesh-Based Visual LocalizationCode2
Learning to Produce Semi-dense Correspondences for Visual LocalizationCode2
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View CompletionCode2
Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose InitializationCode2
Reconstructing People, Places, and CamerasCode2
Enhancing Soccer Camera Calibration Through Keypoint ExploitationCode2
Few-View Object Reconstruction with Unknown Categories and Camera PosesCode1
BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric ReasoningCode1
Benchmarking Image Retrieval for Visual LocalizationCode1
Extreme Two-View Geometry From Object Poses with Diffusion ModelsCode1
Level-S^2fM: Structure from Motion on Neural Level Set of Implicit SurfacesCode1
Towards Accurate Active Camera LocalizationCode1
Back to the Feature: Learning Robust Camera Localization from Pixels to PoseCode1
Learning How To Robustly Estimate Camera Pose in Endoscopic VideosCode1
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
Event-based Stereo Visual OdometryCode1
Activating Self-Attention for Multi-Scene Absolute Pose RegressionCode1
EVLoc: Event-based Visual Localization in LiDAR Maps via Event-Depth RegistrationCode1
Learning to Filter Outlier Edges in Global SfMCode1
Collaborative Dense SLAMCode1
Progressive Correspondence Pruning by Consensus LearningCode1
How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D LinesCode1
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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