InfluPose: Learning State-Conditioned Geometric Influence for Category-Level Object Pose Estimation

Correspondence-based category-level pose estimation recovers an unseen object’s pose and size from matches between its visible surface and canonical coordinates. Even reliable matches can differ in their geometric role. A broad surface may provide redundant evidence, whereas a small distinctive region can resolve an ambiguity. We introduce InfluPose, which learns this regional influence from interventions on paired complete training geometry. We delete and refill regions, measure the response of a frozen estimator, and combine the resulting changes with a dense geometric proxy. Four target channels describe orthogonal rotation, axial rotation, translation, and size. A point-cloud-only predictor learns these targets from partial observations. It combines influence with correspondence reliability in weighted Kabsch initialization and joint pose-and-size refinement. The pose objective trains the predictor to use this estimator-specific response field for recovery. Complete geometry is required only for training. On unseen object instances from REAL275 and HouseCat6D, InfluPose obtains the highest strict-pose mAP among the compared point-cloud methods. Matched REAL275 ablations show gains from regional supervision over learning the influence coefficients through the endpoint objective alone.