Abstract: This study aims to improve the slicing accuracy and substantial material savings in frozen pork ribs. Three-dimensional reconstruction was also proposed for the quantitative program slicing on porcine large chops. Both the precision and efficiency of the slicing were optimized to enhance resource utilization in the meat processing industry. Advanced technologies were selected to revolutionize traditional meat slicing, ultimately contributing to operational efficiency and cost savings. Several key stages were utilized for the accurate and efficient slicing of the pork ribs. Initially, an experimental platform was developed for the point cloud acquisition. A line laser scanner was employed to capture the point cloud data of frozen pork ribs from multiple angles. The raw data was acquired to preprocess the point cloud. Data collection was realized to extract and isolate the target point clouds necessary for subsequent analysis. The data filtered out the noise and irrelevant information for accurate three-dimensional reconstruction. A registration was then conducted to accurately align the data because the point clouds were acquired from different viewpoints after the preprocessing. A rectangular target block was also utilized to accomplish the alignment in conjunction with the Trimmed Iterative Closest Point (ICP). The rectangular target block then acted as a reference point to align the various point clouds, while the Trimmed ICP algorithm facilitated the precise registration of the data. As such, all point clouds corresponded accurately to the actual physical structure of the pork ribs. Accurate alignment was essential to create a reliable three-dimensional model. The volume of the frozen pork ribs was then estimated after alignment. Graham scan algorithm was used to efficiently compute the convex hull of the point cloud, in order to determine the outer boundaries of the objects. The body slicing was segmented and then accumulated to accurately estimate the volume of irregularly shaped objects, like pork ribs. As such, the precise volume estimation was achieved to determine the appropriate slicing parameters for quality control and consistency in the final product. Finally, the required slicing thickness was calculated, according to the predefined quality requirements. At the same time, it was assumed that the frozen pork ribs exhibited a uniform density distribution throughout their mass. The thickness of each slice was also computed to fully meet the specified quality standards. The slicing process resulted in uniform and high-quality slices of meat. Quantitative slicing experiments were effectively conducted to produce the slices, according to the desired quality metrics. A series of tests were conducted on 15 sets of frozen pork rib samples, in order to validate the effectiveness and reliability of the quantitative slicing. The experimental results indicated that the average absolute error between the actual and the target slice quality was recorded at 8.62 g, with an impressive average relative error of 7.41%. Furthermore, the compliance rate of 85.67% was achieved after slicing. These findings confirmed that the quantitative slicing was effective and accurate in real-world applications. The successful implementation of quantitative slicing can also provide a viable solution to enhance the slicing operations in the meat processing industry. Additionally, the findings can also contribute significantly to the advancement of smart technology. The material wastage was also minimized to maintain the high slicing accuracy. This slicing technique can be integrated to enhance the operational efficiency, product quality, and resource utilization in meat processing facilities, ultimately leading to greater sustainability during operations. This research can also open the avenues to further explore and apply three-dimensional reconstruction in various aspects of food processing and quality evaluation.