What Do Scan-Derived Class Prototypes Add? Disentangling Supervision, Prototype Content and Query Protocol in Recognition over Frozen Foundation Features

arXiv:2609.04381 · cs.CV, cs.AI, cs.RO · Submitted 2026-09-03 · Read on arXiv

cs.CV, cs.AI, cs.RO

Submitted: 2026-09-03

Updated: 2026-10-01

Comments: 19 pages, 11 figures, 5 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 37× in this paper · explained in 1 episode
• Gaussian Splatting — 15× in this paper · explained in 4 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 7× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• 6D Object Pose Estimation — 6× in this paper · explained in 1 episode
• Foundation Model — 4× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• 3D Gaussian Splatting (3DGS) — 2× in this paper · explained in 4 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• European Conference on Computer Vision — 2× in this paper
• Alignment — 1× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 1× in this paper
• Cooperation — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 1× in this paper
• Perplexity — 1× in this paper · explained in 3 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes

Sources

• Textureless Object Recognition: An Edge-based Approach
• GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024
• BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation
• DINOv3
• Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

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