Reconstructing occluded objects is essential for accurate product size estimation in industrial and agricultural contexts, where occlusions often result from overlapping items or environmental clutter. Existing approaches often rely on class-specific priors, which limits their generalization across diverse product types and occlusion patterns. In this work, we present ReDO-Net, a novel shape-conditioned pipeline for 2D and 3D object completion under occlusion. Our approach encodes visible object shapes into a continuous latent space using a Variational Autoencoder (ReDO-VAE), which guides a U-Net-based module (ReDO-UNet) for amodal silhouette reconstruction. A dedicated GAN module (ReDO-GAN) then performs depth inpainting to recover full 3D shape information. Unlike class-conditioned methods, ReDO-Net leverages geometric priors directly from partial observations, allowing robust and category-agnostic completion. Experiments on both synthetic and real-world datasets demonstrate significant improvements over baselines: a 15% gain in 2D completion accuracy and high-quality 3D reconstructions, achieving an RMSE of 0.083 on occluded vegetable samples. To promote reproducibility, we release Fruits360Occlu, a public dataset with strong occlusions tailored for amodal mask on agricultural products.