MatchU: 6D Pose Estimation from RGB-D Images
MatchU proposes a Fuse-Describe-Match strategy for 6D pose estimation from RGB-D images, surpassing existing methods in accuracy and speed without the need for re-training. The approach fuses 2D texture and 3D geometric cues to predict poses of unseen objects.
GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence
GigaPose is a fast, robust, and accurate method for CAD-based novel object pose estimation in RGB images.
GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence
GigaPoseは、CADベースの新しいオブジェクトポーズ推定において高速で堅牢かつ正確な手法を提供する。
MV-ROPE: Multi-view Constraints for Robust Category-level Object Pose and Size Estimation
Utilizing multi-view RGB video streams, MV-ROPE provides a novel framework for robust and accurate category-level object pose estimation.
KITchen: A Real-World Benchmark and Dataset for 6D Object Pose Estimation in Kitchen Environments
Addressing the gap in 6D object pose estimation methods by introducing a novel benchmark and dataset specifically designed for kitchen environments.
KITchen: A Real-World Benchmark and Dataset for 6D Object Pose Estimation in Kitchen Environments
Real-world benchmark and dataset for 6D object pose estimation in kitchen environments.
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