Learning Shared Pose Quality For Recursive 6D Object Tracking

Quality-driven decisions in recursive 6D tracking change the hypotheses that the same system must later assess. We study this feedback using a shared model that predicts object-size-normalized ADD-S error for visual observations and temporal priors. Development-calibrated risk determines candidate eligibility, relative predicted error controls fusion, and fresh registration renews an unreliable observation–prior pair. Accepted outputs update motion history, while used registrations reseed the observer. We then refit the quality model once on development candidates encountered under the induced policy. On five post-development sequences from three objects, Full improves sequence-macro AUC by 6.90 points over Matched simple under matched renewal schedules. In a separate four-condition autonomous diagnostic, Full reaches 79.99 versus 58.16 AUC against a development-tuned Autonomous simple policy, but requires 4,477 versus 452 registration calls and does not improve every object. Disabling reseeding reduces mean AUC from 86.33 to 69.76. Fixed-candidate controls reveal distribution-specific refit gains and degradation under distribution shift. Overall, the results characterize the accuracy–cost trade-offs and failure boundaries of quality-driven recursive tracking rather than establishing cost-matched autonomous superiority or broad generalization.