AxisPose++: Reference-Anchored Tri-Axis Transport for Single-Reference Unseen Object Pose Estimation

Estimating the unseen object's pose from minimal visual observations is essential for scalable robotic perception. While single-reference methods have shown promise, existing approaches typically rely on novel-view template synthesis, predicted geometry and local matching, or direct relative-pose regression. These pipelines remain sensitive to occlusion and weak texture and may accumulate errors through intermediate predictions. More fundamentally, the reference is primarily used as visual evidence rather than as an explicit coordinate anchor. We present AxisPose++, a reference-anchored paradigm for unseen object pose estimation from a single RGB reference. AxisPose++ formulates the task as coordinate transport: the posed reference anchors the object coordinate frame, whose compact tri-axis structure is directly transported to the target view. A geometry-grounded back-projection module then recovers the target rotation by enforcing axis orthogonality and camera-projection consistency. By avoiding costly intermediate synthesis and correspondence construction, AxisPose++ enables efficient geometry-grounded rotation recovery. Extensive experiments on synthetic and real-world benchmarks demonstrate strong generalization while being $43\times$ faster than the current SOTA OrienPose. Anonymous code is available at https://anonymous-pose.github.io/axispose_pp.github.io/