Technological advancements in computer vision and machine learning are transforming garment design and reconstruction processes across various industries and domains, including fashion, virtual try-ons, and historical preservation. With developments in parametric modelling, garment reconstruction has evolved from basic shapes to highly detailed representations. Despite these advancements, current techniques often overlook essential detailed features of the garment, such as buttons, collars, hems and pockets, leading to a simplistic and generalized digital representation of the garment. This overlook causes a gap in achieving fully realistic digital garment models. To address this issue, this thesis, made in collaboration with the Korea Advance Institute of Technology (KAIST), investigates garment reconstruction through a novel approach that integrates detailed parametric modelling for top garments.