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Start with the room shell
Trace the floor-to-wall and wall-to-ceiling lines around the image. Follow each corner and ask whether the surfaces meet consistently, whether wall thickness looks plausible, and whether doors and windows appear to sit within the walls rather than melt into them.
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- Check whether doors and windows have coherent frames, sills and heads.
- Compare opening heights with nearby furniture and with other views of the same project.
- Look for openings that appear to lead nowhere or for wall junctions that cannot belong to the same room.
These are prompts for closer inspection, not proof that an image is wrong. OpenAI describes using an untextured solid view in a Blender example to inspect room layout, openings and connections. The team said those checks supported review, but did not certify every detail: OpenAI’s architectural visualization example.
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Use familiar objects to judge scale
Furniture and everyday fittings provide reference points even when the image gives no dimensions. Compare the apparent size and placement of a door, chair, countertop, table, bed, light switch or outlet with nearby objects.
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- A sofa that changes size across the image without a clear perspective explanation may indicate scale drift.
- A countertop that seems unusually high next to a chair, or a bed that crowds a doorway, deserves a dimension check.
- A corridor that appears to change width along its length may be distorted by perspective or by inconsistent geometry.
Do not treat these impressions as fixed dimensional rules. Wide-angle views can exaggerate room depth and distort apparent proportions, so compare against a plan, model or stated dimensions when fit matters. The Perspective Research Centre’s guide to AI-generated images discusses misleading room dimensions and perspective cues.
Test whether the perspective holds together
Architectural lines should recede in compatible directions from a given viewpoint. Compare vertical edges, cabinet runs, window mullions, tile joints and floorboards. Repeated elements can reveal whether the image maintains one coherent perspective or merely imitates the look of one.
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- Extend parallel edges mentally: do they appear to converge toward compatible vanishing directions?
- Check whether repeated cabinets, tiles or mullions keep consistent spacing and scale.
- Compare floor and ceiling lines with the apparent orientation of walls and openings.
A single image may look spatially convincing without mapping to a coherent, measurable 3D scene. Visual intuition alone cannot establish exact geometry; consult a plan, grid, depth reference or model for consequential checks. The Perspective Research Centre notes that the absence of obvious anomalies does not prove an image is authentic.
Follow shadows, reflections and overlaps
Lighting and surface effects can make a render feel realistic while concealing spatial contradictions. Check whether cast shadows agree with the apparent windows and light fixtures, and whether reflective surfaces show something plausible from the camera’s viewpoint.
- Inspect mirrors, glass, glossy counters and metal for reflections that seem to come from an absent object or an incompatible angle.
- Check overlaps: a chair leg should not pass through another object or disappear into a rug without a plausible explanation.
- Look for textures or repeated patterns that change scale abruptly, and for depth of field that seems inconsistent across the scene.
Incompatible shadows, reflections, vanishing directions, object scale, occlusion and depth of field are warning signs to investigate, not standalone proof of AI generation or bad design. Examine both the whole room and local details; surface polish can draw attention away from either.
Compare views and project information
If several views are meant to show the same design, compare furniture identity, placement, geometry and scale from one image to the next. A window, wall or piece of furniture should not silently change between views if the images claim to depict one source design.
For a real project, compare consequential features with the plan, sections, model and stated dimensions. A geometry inspection can help review a view, but it is not a guarantee that every intersection or architectural detail is correct. In OpenAI’s Blender example, the team described its geometry checks this way: “They didn’t replace looking at the renders, and they weren’t a certification that every possible intersection or architectural detail was correct.”
Know what kind of AI workflow produced the image
The useful distinction is not simply “AI” versus “non-AI”; it is what geometry, if any, constrained the output and what the image is intended to communicate.
| Question | Prompt-only image generation | Model-integrated visualization |
|---|---|---|
| Geometry input | Prompts or image guidance; the workflow may not know the project’s measured model or design intent. | A host design model can provide source geometry. |
| What may be constrained | Walls, openings, camera positions and furniture locations are not necessarily held to a project model. | Geometry can be constrained by the model, depending on the tool and workflow. |
| What can still drift | The image may reinterpret the room and its details. | Materials, lighting and context can still diverge from a physically based render; model integration does not guarantee every detail is accurate. |
| Best-fit use | Mood and concept exploration, with claims checked against project documentation before decisions. | Visualization grounded in model geometry, with consequential details still checked against documentation. |
Chaos, an industry vendor, describes these differences in its September 9, 2026 article on AI rendering workflows for architects. Its guidance is not a building-code rule or an independent evaluation. It recommends labeling AI-assisted visuals in client presentations and treating them as explorations rather than documented design decisions.
Use renders as concepts, not construction evidence
An attractive render is not a substitute for documented dimensions, specified products, construction information or qualified professional review. Treat the image as a concept unless its relevant details are supported by project documentation. If you need to compare a real room with an image, a laser distance measurer can help check dimensions; for exact fit or a consequential decision, rely on a plan or professional measurement, not on the image’s apparent proportions.
There is no established universal checklist that can reliably identify every artifact, or validated detection rate showing how accurately visual inspection identifies AI images. A 2022 study by Joern Ploennigs and Markus Berger analyzed 40 million public Midjourney queries as part of its examination of architectural-design use cases; that is a dataset size, not a count of flawed interiors or an artifact-prevalence statistic. See AI Art in Architecture for the study.
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