Pixels, prompts, and precision: a comparative analysis of AI-assisted and traditional architectural visualization across diverse interior design styles

Abstract

This study examines the practical and perceptual differences between two architectural visualization workflows: Autodesk 3ds Max with Corona Renderer, a physics-based traditional rendering engine, and Fabrie AI combined with Photoshop postproduction, an emerging AI-assisted approach. Both workflows were applied to the same interior reception space, rendered across six distinct design styles under identical spatial geometry, lighting, and camera conditions — isolating the rendering tool as the sole variable under investigation.

Evaluation drew on two complementary evidence streams. Objectively measured performance metrics — including render time, output resolution, and file size — provided a quantitative baseline for workflow comparison. These were paired with structured expert assessments gathered from a panel of architects, interior designers, and client representatives, who evaluated each render against five perceptual dimensions: visual realism, design communication, creator skill, client approval, and overall quality. Inferential statistical tests — paired-samples t-tests, a chi-square goodness-of-fit test, and a one-way ANOVA — were applied to assess the significance and consistency of observed differences, with Cohen's d and η² reported as effect sizes throughout.

The findings reveal a nuanced picture that resists simple conclusions. While 3ds Max maintains a meaningful advantage in photorealistic fidelity — particularly for organic, texture-rich aesthetics such as Wabi Sabi and Mid Century styles — the two tools produce output of statistically indistinguishable overall quality across the full style set. For geometrically structured styles including Bauhaus, Neoclassical, and Industrial interiors, AI-generated renders match or exceed 3ds Max on both quality scores and client approval. These findings challenge the common framing of AI as either a wholesale replacement for or an inadequate substitute for traditional rendering, and instead point toward a strategically hybrid workflow model: one in which each technology is deployed according to the aesthetic demands of the project and the intended purpose of the deliverable.

Beyond its immediate empirical contribution, this research offers the field a replicable mixed-methods evaluation framework, a style-conditioned decision matrix for practitioners navigating tool selection, and a preliminary evidence base for the thoughtful integration of AI visualization tools in both professional practice and architectural education curricula. These conclusions are necessarily tied to the current generation of AI visualization tools; given the rapid pace of development in this field, the relative performance gap documented here should be read as a snapshot rather than a fixed verdict, and may narrow or shift as the underlying models continue to improve. 

Received: 20 June 2026

Accepted: 27 June 2026

Published: 29 June 2026