mlsanityData to 14 Oct 2026

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

arXiv 2609.04784 ↗First posted 4 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

Novel-object 6D pose pipelines depend on front-end hypotheses that retain target instances under clutter and occlusion without overwhelming downstream pose estimation. We introduce Cue-Calibrated Linguistic Object Onboarding (CLON), a training-free proposal-and-matching front-end. CLON converts multi-view CAD renderings into compact linguistic memories that provide object-specific conditioning to a frozen proposal generator. Constructed offline and reused across scenes, these memories augment generic prompts before segmentation; subsequent template verification assigns object identity. As a complementary onboarding step, CLON derives fixed cue weights from template statistics for semantic, appearance, and geometric verification. On seven BOP-Classic-Core datasets, CLON improves detection AP by 8.2 percentage points (pp), segmentation AP by 6.2 pp, and downstream pose AR by up to 4.1 pp over the evaluated CNOS and SAM-6D front-ends. In a real-world localization-to-grasp study under clutter and occlusion with 100 trials per method, CLON's improved localization doubles grasp success relative to the strongest evaluated baseline. Controlled ablations evaluate linguistic conditioning, multi-view construction, and verification. Analysis of a smaller candidate set with higher class-agnostic recall on five datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 30 references that are in the map. First posted on arXiv 4 Sep 2026.

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26 of 30 references.

References · 26

  1. 2023Segment Anythingcited by 8,234
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  4. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  5. 2025DINOv3cited by 1,414
  6. 2025SAM 3: Segment Anything with Conceptscited by 906
  7. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 775
  8. 2025Depth Anything 3: Recovering the Visual Space from Any Viewscited by 692
  9. 2023FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objectscited by 429
  10. 2018BOP: Benchmark for 6D Object Pose Estimationcited by 254
  11. 2017T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objectscited by 210
  12. 2022MegaPose: 6D Pose Estimation of Novel Objects via Render & Comparecited by 201
14 more
  1. 2022Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Imagescited by 121
  2. 2023SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimationcited by 117
  3. 2023GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondencecited by 114
  4. 2019HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objectscited by 110
  5. 2023FoundPose: Unseen Object Pose Estimation with Foundation Featurescited by 109
  6. 2023CNOS: A Strong Baseline for CAD-based Novel Object Segmentationcited by 87
  7. 2015Recovering 6D Object Pose and Predicting Next-Best-View in the Crowdcited by 79
  8. 2023BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objectscited by 70
  9. 2023FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation modelscited by 57
  10. 2025Any6D: Model-free 6D Pose Estimation of Novel Objectscited by 49
  11. 2025BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimationcited by 19
  12. 2024Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentationcited by 17
  13. 2025OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Promptscited by 15
  14. 2025SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variationscited by 3

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