Deformation-driven CBCT reconstruction techniques can generate accurate and high-quality CBCTs from deforming prior CTs using sparse-view cone-beam projections. The solved deformation-vector-fields (DVFs) also propagate tumor contours from prior CTs, which allows automatic localization of low-contrast liver tumors on CBCTs. To solve the DVFs, the deformation-driven techniques generate digitally-reconstructed-radiographs (DRRs) from the deformed image to compare with acquired cone-beam projections, and use their intensity mismatch as a metric to evaluate and optimize the DVFs. To boost the deformation accuracy at low-contrast liver tumor regions where limited intensity information exists, we incorporated biomechanical modeling into the deformation-driven CBCT reconstruction process. Biomechanical modeling solves the deformation on the basis of material geometric and elastic properties, enabling accurate deformation in a low-contrast context. Moreover, real clinical cone-beam projections contain amplified scatter and noise than DRRs. These degrading signals are complex, non-linear in nature and can reduce the accuracy of deformation-driven CBCT reconstruction. Conventional correction methods towards these signals like linear fitting lead to over-simplification and sub-optimal results. To address this issue, this study applied deep learning to derive an intensity mapping scheme between cone-beam projections and DRRs for cone-beam projection intensity correction prior to CBCT reconstructions. Evaluated by 10 liver imaging sets, the proposed technique achieved accurate liver CBCT reconstruction and localized the tumors to an accuracy of ∼1 mm, with average DICE coefficient over 0.8. Incorporating biomechanical modeling and deep learning, the deformation-driven technique allows accurate liver CBCT reconstruction from sparse-view projections, and accurate deformation of low-contrast areas for automatic tumor localization.