perceiving-objects
perceiving-objects is a Computer vision skill for AI agents, published by graph-robots in the open-robot-skills repository on GitHub. Skillspace rates perceiving-objects C (78/100) and it passed the safety scan; the rating is based on static analysis only.
Fast single-path 3D object perception. Runs Grounding-DINO broad detection, a pairwise VLM crop tournament to identify the target box, SAM3 box segmentation, and depth back-projection to a world-frame point cloud, finished by geometry.filter_and_compute_obb for a clean oriented bounding box, mask, and cloud. Multi-camera rigs get a verified wrist-camera fallback gate. Use when a manipulation workflow needs to localize one named object quickly — uncluttered scenes with visually distinct targets, latency-bound loops, or platforms deploying only DINO + VLM + SAM3 + geometry.
- Grade
- C
- Quality
- 78 / 100
- Safety
- Passed
- GitHub stars
- 22
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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