arXiv:2608. 20056v1 Announce Type: new Abstract: Inertial measurement units (IMUs) are now standard in most consumer devices, such as smartphones, drones, and extended reality (XR) headsets.
arXiv:2609.00713v1 Announce Type: new Abstract: Estimation of the absolute pose of an object is an essential task for various robotic applications. Recently, incorporating gravity direction as prior...
arXiv:2208.12513v4 Announce Type: replace Abstract: In computer vision, camera pose estimation from correspondences between 3D geometric entities and their projections into the image has been a widel...
arXiv:2609.24424v1 Announce Type: new Abstract: Monocular RGB-based hand pose estimation has emerged as a critical research frontier in computer vision. The local hand pose estimation methods predict...
Monocular depth estimation is a fundamental prerequisite for 3D reconstruction and autonomous navigation in Unmanned Aerial Vehicles (UAVs). In practical deployments, UAVs operate under highly dynamic camera poses characterized by continuous variations in height, pitch, roll, and field of view (FOV).
arXiv:2608.21926v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) navigation in modern low-altitude environments requires more accurate pose alignment in the final approach stage for targ...
arXiv:2609.24482v1 Announce Type: new Abstract: Monocular 3D human pose estimation (HPE) remains challenging due to depth ambiguity, occlu- sions, and the need for temporal consistency. While multi-v...
arXiv:2608.21066v1 Announce Type: new Abstract: Deploying autonomous systems in safety-critical domains demands guaranteed robustness against physically plausible geometric perturbations rather than...
The paper proposes a data‑centric optimization for object pose estimation that uses a physically grounded rotation representation based on principal axes alignment. By aligning an object's coordinate system with its inertial principal axes, the method achieves inherent stability, symmetry‑aware canonicalization, and framework agnosticism, allowing it to be applied at the dataset level without modifying existing networks. Experiments on category‑level and instance‑level models show consistent accuracy improvements while preserving baseline network integrity.
The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.
arXiv:2601.13913v3 Announce Type: replace Abstract: We consider monocular 3D human pose estimation (HPE), where the goal is to predict 3D human skeletal joints from a single 2D image, typically via 2...
arXiv:2607.09985v2 Announce Type: replace Abstract: Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, generalizatio...
arXiv:2607. 13449v1 Announce Type: cross Abstract: 6-DoF pose estimation is a critical task in autonomous rendezvous and proximity operations.