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Cone-Beam Computed Tomography (CBCT) is widely used for diagnostics and treatment planning in oral and maxillofacial field due to its low radiation dose and high spatial resolution. Still, its clinical utility is limited by low contrast and incorrect Hounsfield Unit (HU) values. In contrast, multi-detector CT (CT) provides high contrast and reliable HU measurements, with a higher radiation dose. In this work, we present a novel two-stage framework for unpaired CBCT-to-CT synthesis that ensures the exact preservation of anatomical structure, maintains high resolution, and achieves accurate HU value. In thefirst stage, we generate pseudo-paired CT images. In the second stage, weutilize a UNet++ generator enhanced with Interpolation and Convolution Upsampling (ICUP), Edge-Conditioned Skip Connections (ECSC), and a dual discriminator strategy for a semi-supervised approach. Consequently, we generate realistic CT images using pseudo-paired CT images. Extensive quantitative and qualitative evaluations demonstrate that our method outperforms existing unpaired translation techniques, producing realistic CT images that closely match CT images in both HU accuracy and exactly preserve anatomical structure of the CBCT. The code is available at https://github.com/HANJIYONG/Semi-Supervised-Deformation-Free-I2I.