Handoff-H1: An Orchestrated Vision-Agent System for Material Quantity Takeoff from Construction Blueprints
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Abstract (arXiv)
Converting a set of architectural blueprints into a complete material quantity takeoff requires visual perception across drawing sheets, dimensional and multi-hop reasoning, and grounding in construction conventions that the drawings never state. We present Handoff-H1, a takeoff system built from three layers: purpose-built computer-vision models that extract primitives; tool-using agents equipped with image operations and in-house visual-task tools, including CV-model-backed counting, detection and plan decomposition; and a persistent, hierarchically structured project foundation, grounded in a curated construction knowledge base. We evaluate on the Construction Blueprint Takeoff Benchmark: 10 real residential blueprint sets paired with consensus-validated expert takeoffs - 2,009 verified line items, restricted for scoring to the 1,348 primary-tier materials that drive an estimate - scored per trade by an LLM judge on material coverage and quantity Precision@25% ([email protected]) and combined into a weighted composite. Under identical scoring from the raw PDF, seven frontier and open-weight models span composites of 35-61, and independent professional estimators - scored against the same reconciled gold standard - post 77.6% (65.5% coverage, 87.9% [email protected]). Handoff-H1, working end-to-end from the raw PDF, reaches 81.6% (86.1% coverage, 78.8% [email protected]): roughly 20 points above the strongest frontier agent, and above the independent estimators by pairing near-human quantity precision with coverage they do not reach. The evaluation harness is public for the open harbor framework; the blueprint sets and ground truth are available upon request for research use.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 15 Aug 2026.
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References · 16
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4 more
- 2025Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBenchcited by 6
- 2025Blueprint-Bench: Comparing spatial intelligence of LLMs, agents and image modelscited by 5
- 2019Automation of Construction Quantity Take-Off: Using Building Information Modeling (BIM)cited by 1
- 2026Multimodal Language Models Cannot Spot Spatial Inconsistenciescited by 1
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