Struct-Align: Zero-Shot Text-to-3D Scene Retrieval via Locality-Aware Structural Alignment

Abstract

Text-to-3D Scene Retrieval (T3SR) aims to retrieve 3D scenes that match users' linguistic queries, enabling intuitive access to 3D scene repositories. Existing approaches rely on joint embedding learning with large amounts of paired text–scene data, which is expensive to collect and often fails to generalize under open-vocabulary queries and diverse scene distributions. In this paper, we propose Struct-Align, a foundation-model-driven framework for zero-shot T3SR that eliminates the need for paired training data. Our key insight is to reformulate T3SR as a single-modality structural alignment problem by converting both 3D scenes and textual queries into a shared, schema-aligned textual representation compatible with pretrained text embedding models. To reliably derive such representations from complex 3D environments, we introduce a role-decomposed scene structuring pipeline that mitigates generative instability and produces semantically consistent scene depictions. To address the inherent semantic asymmetry between query and scene representations, we further propose a locality-aware structural matching strategy that explicitly localizes query intent and performs instance- and relation-level alignment within query-relevant substructures. Extensive experiments on multiple benchmarks demonstrate that Struct-Align outperforms both training-based and zero-shot baselines while exhibiting strong robustness to domain shift.