Passive Multi-View 3D Reconstruction for Immersive Foreign-Language Interpreting Training Assessment in Intelligent Education: Balancing Cross-Scale Symmetry and Observation Asymmetry Under Non-Ideal Consumer-Camera Acquisition

In intelligent education, reusable immersive scenes can provide consistent visual contexts for foreign-language interpreting practice and structured assessment activities. This work focuses on the optical and geometric stage of such scene preparation: passive multi-view 3D reconstruction from non-ideal consumer-camera imagery. The PatchMatchNet pipeline incorporates PANet-style bidirectional cross-scale feature exchange and CBAM weighting of aggregated matching evidence to balance reciprocal geometric information with spatially variable observation reliability. The reported allocation comprises 99 DTU scenes and 44 assigned self-collected scenes, with 5 additional self-collected scenes listed outside the allocation. In the joint-module comparison, the reported reconstruction-to-reference, reference-to-reconstruction, and symmetric-mean point-set distances change from 0.483, 0.321, and 0.402 to 0.350, 0.222, and 0.286, respectively (nominal mm). These values characterize the custom mixed evaluation and its within-table architectural comparison. The reconstruction, point-cloud export, and Unity3D (version 2022.3 LTS) visualization workflow is illustrated on the PICO 4 Pro, supporting the preparation of visual assets for immersive language-learning environments.