# CrossProjection: Geometric Grounding Beyond Viewpoint Change in Architectural Drawings

arXiv: https://arxiv.org/abs/2608.00473 · HTML: https://mlsanity.com/paper/2608.00473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 1 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence](https://mlsanity.com/paper/2505.23764/) (2025)
- [Towards Cross-View Point Correspondence in Vision-Language Models](https://mlsanity.com/paper/2512.04686/) (2025)
- [AECV-Bench: Benchmarking Multimodal Models on Architectural and Engineering Drawings Understanding](https://mlsanity.com/paper/2601.04819/) (2026)
- [CReFT-CAD: Boosting Orthographic Projection Reasoning for CAD via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.00568/) (2025)
- [AEC-Bench: A Multimodal Benchmark for Agentic Systems in Architecture, Engineering, and Construction](https://mlsanity.com/paper/2603.29199/) (2026)
- [Blueprint-Bench: Comparing spatial intelligence of LLMs, agents and image models](https://mlsanity.com/paper/2509.25229/) (2025)
- [3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models](https://mlsanity.com/paper/2603.07751/) (2026)
- [MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding](https://mlsanity.com/paper/2605.30794/) (2026)
- [MindEdit-Bench: Benchmarking Object-Level Counterfactual Spatial Reasoning in VLMs from In-the-Wild Photos](https://mlsanity.com/paper/2607.00491/) (2026)
- [CVT-Bench: Probing Spatial-State Integrity through Counterfactual Viewpoint Transformations](https://mlsanity.com/paper/2603.21114/) (2026)
- [ArchSIBench: Benchmarking the Architectural Spatial Intelligence of Vision-Language Models](https://mlsanity.com/paper/2605.20837/) (2026)
- [TriViewBench: Controlled Complexity Scaling for Multi-View Structural Reasoning in MLLMs](https://mlsanity.com/paper/2606.26029/) (2026)

## Abstract (arXiv)

Architectural drawings violate the usual assumption behind multi-view reasoning: plans and sections are cuts, while elevations are facade projections, so corresponding components change appearance in ways camera motion cannot explain. We introduce CrossProjection, an anchor-grounded diagnostic of whether vision-language models preserve component identity and externalize geometry across heterogeneous architectural views. It evaluates Matching, Registration, and Geometric Grounding through categorical judgments, candidate selection, and free point, line, and region localization. Across 23 real drawing sets and 1,954 categorical conditions per model, GPT-5.5 scores 82.4%, Qwen3-VL-32B-Instruct 62.2%, and GLM-4.5V 57.2%. A matched 200-target study crosses natural and vector-text-suppressed drawings with closed-candidate and free-geometry outputs. Candidate-supported performance is often higher, but free localization remains fragile: on natural drawings, point/region PCK@.05 is 54-76% for GPT, 8-10% for Qwen, and 14-36% for GLM; line endpoint PCK@.05 is 22%, 4%, and 0%. A coordinate grid recovers some GPT point/region precision but not lines. Three architecture-trained participants reach 87.3-93.3% categorical accuracy and 76-92% GT-region hit, supporting task feasibility rather than a population-level human ceiling. Because the categorical families do not form a same-item Matching-Registration contrast and interface controls alter multiple burdens, we avoid mechanistic claims. The supported conclusion is narrower: closed-choice or marked-element success does not entail reliable explicit geometric grounding. For drawing-guided CAD/BIM systems, categorical correctness should not be treated as evidence of candidate-free spatial reliability. Reusable on-sheet anchors, fixed-denominator scoring, and hash-locked artifacts establish an audit trail for this gap.