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arXiv Computer Vision By Enrico Saccon, Tommaso Faraci, I\~{n}igo De La Ossa Zarzuelo, Luigi Palopoli, Marco Roveri, Matteo Saveriano

VLMs Can Describe, But Not Measure: Object-Centric Scene Understanding for Robotic Manipulation

Read the original on arXiv Computer Vision →

The paper introduces a modular perception framework that uses vision‑language models (VLMs) to annotate object‑level regions from a single RGB‑D observation, then grounds these annotations with depth data to build an object‑centric representation. Experiments on 151 tabletop scenes demonstrate that this decomposition maintains strong semantic performance while significantly improving localization and depth estimation compared to direct VLM inference. The resulting representation is integrated into a task‑planning system for robotic manipulation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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