TL;DR
A two-stage method for accurate 6D pose estimation using consumer-grade depth cameras in real-world scenarios with YOLO-based detection, Euclidean clustering, and moving least squares smoothing combined to extract high-quality target point clouds from noisy RGB-D observations is proposed.
PIXIE is a zero-shot framework that estimates the 6D pose of an object from an RGB image using only an untextured 3D model, inherently robust to lighting and texture variation, while correspondence filtering handles geometric deviations between the model and physical object.
A complete, reproducible RGB-only pipeline and ablation for geometry-first and estimated-depth pseudo-LiDAR is used as a controlled test of one hypothesis: that cross-view geometric consistency, not monocular depth accuracy, governs performance under Sim2Real.
A unified calibration-free 3D MCPT framework that infers geometric structure directly from visual data using deep foundation models is proposed, establishing a strong baseline for purely vision-based 3D tracking.
A robust and accurate minimal solver that estimates camera pose from only two LED markers by incorporating known tilt angle and camera height measured by an onboard sensor, such as an IMU or an altimeter is derived.
This paper studies monocular 6D pose estimation of small cubic objects from a single RGB image and proposes a two-stage manipulation- oriented framework, which achieves the strongest overall balance in ADD-S, translation accuracy, rotation stability, and task-oriented usability metrics.
This work proposes an improved geometric hashing method that robustly estimates the rigid transformation in the presence of noise and outliers, and demonstrates its effectiveness and speed using both simulated and real-world datasets.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
