CLON: Cue-Calibrated Linguistic Object Onboarding for Zero-Shot 6D Pose Front-Ends

2026-09-04

AI Summary

本文提出CLON方法，通过构建语言语义记忆和对象集线索权重来改进零样本6D姿态估计的前端性能，提高检测、分割和姿态估计精度。

Abstract

Zero-shot 6D pose estimation pipelines increasingly rely on strong downstream pose solvers, but their performance is often limited by the front-end: object proposals must preserve partially visible true positives while rejecting semantically plausible distractors. We introduce Cue-Calibrated Linguistic Object Onboarding (CLON), a front-end requiring no task-specific training for new objects. Given rendered templates of the onboarded object set, CLON constructs a linguistic semantic memory for top-down proposal generation and object-set cue weights for calibrated proposal scoring. The linguistic memory guides SAM 3 toward high-recall proposals for onboarded objects, while cue weights are computed once from the onboarded object set before scene inference and kept fixed during online scoring. On seven BOP-Classic-Core datasets, CLON improves detection AP by 8.1 percentage points (pp), segmentation AP by 6.2 pp, and downstream 6D pose AR by up to 4.1 pp over CNOS and SAM-6D front-ends.

Problem

• Zero-shot 6D pose estimation
• front-end
• object proposals
• true positives
• distractors

Innovation

• Zero-shot 6D pose estimation
• Linguistic Semantic Memory
• Cue Weights
• Proposal Generation
• High-Recall Proposals