arXiv:1511.01156
공식 코드 · Papers with Code
Robust Large-Scale Localization in 3D Point Clouds Revisited
원문 초록
We tackle the problem of getting a full 6-DOF pose estimation of a query image inside a given point cloud. This technical report re-evaluates the algorithms proposed by Y. Li et al. "Worldwide Pose Estimation using 3D Point Cloud". Our code computes poses from 3 or 4 points, with both known and unknown focal length. The results can easily be displayed and analyzed with Meshlab. We found both advantages and shortcomings of the methods proposed. Furthermore, additional priors and parameters for point selection, RANSAC and pose quality estimate (inlier test) are proposed and applied.
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DeepSTEP: Simultaneous Tracking and Estimation of Pose for Animals and Humans
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation
SpatialTrackerV2: 3D Point Tracking Made Easy
GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft
VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation
토론
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