Abstract: To address the pressing challenges of low slicing accuracy and substantial material wastage associated with the slicing of frozen pork ribs, a groundbreaking method founded on three-dimensional reconstruction for quantitative slicing has been proposed. This innovative approach aims to enhance both the precision and efficiency of the slicing process, thereby optimizing resource utilization within the meat processing industry. By leveraging advanced technologies, the proposed method seeks to revolutionize traditional meat slicing techniques, ultimately contributing to increased operational efficiency and reduced costs.The proposed methodology comprises several key stages that collectively ensure accurate and efficient slicing of the pork ribs. Initially, a dedicated experimental platform for point cloud acquisition was developed. This platform employed a sophisticated line laser scanner to capture point cloud data of frozen pork ribs from multiple angles, ensuring comprehensive data collection for analysis. The raw data acquired underwent a thorough point cloud preprocessing phase, which was critical for extracting and isolating the target point clouds necessary for subsequent analysis. This preprocessing step is vital, as it significantly prepares the data for accurate three-dimensional reconstruction by filtering out noise and irrelevant information.Following the preprocessing stage, the acquired point clouds from different viewpoints underwent a meticulous registration process to align the data accurately. This alignment was accomplished through the strategic use of a rectangular target block in conjunction with the Trimmed Iterative Closest Point (ICP) algorithm. The rectangular target block acted as a reference point for aligning the various point clouds, while the Trimmed ICP algorithm facilitated precise registration of the data, ensuring that all point clouds corresponded accurately to the actual physical structure of the pork ribs. Achieving accurate alignment is essential for creating a reliable three-dimensional model, which serves as a foundation for the subsequent analysis and processing steps.Once the point clouds were successfully aligned, the next phase involved estimating the volume of the frozen pork ribs. This estimation was conducted using the Graham scan algorithm, combined with an innovative irregular body slicing segmentation accumulation method. The Graham scan algorithm efficiently computed the convex hull of the point cloud, which is instrumental in determining the outer boundaries of the object. Meanwhile, the segmentation accumulation method provided a robust means for accurately estimating the volume of irregularly shaped objects like pork ribs. By integrating these techniques, a precise volume estimation was achieved, which is critical for determining the appropriate slicing parameters necessary for quality control and consistency in the final product.The final stage of the proposed method concentrated on calculating the required slicing thickness based on predefined quality requirements. In this process, it was assumed that the frozen pork ribs exhibited a uniform density distribution throughout their mass. Utilizing this assumption, the thickness of each slice was computed to meet the specified quality standards, ensuring that the slicing process resulted in uniform and high-quality slices of meat. This calculation was paramount for executing quantitative slicing experiments effectively, as it ensured that the slices produced adhered closely to the desired quality metrics.To validate the effectiveness and reliability of the proposed quantitative slicing method, a comprehensive series of tests was conducted on 15 sets of frozen pork rib samples. The results of these experiments indicated that the average absolute error between the actual slice quality and the target quality standard was recorded at 8.62 grams, with an impressive average relative error of 7.41%. Furthermore, the method achieved a noteworthy compliance rate of 85.67%. These findings strongly confirm that the proposed quantitative slicing method is not only effective but also accurate in real-world applications. The successful implementation of this method provides a viable solution for enhancing slicing operations in the meat processing industry. Additionally, it contributes significantly to the advancement of smart technology within this sector. The method’s ability to minimize material wastage while maintaining high slicing accuracy underscores its profound potential impact on improving operational efficiency and resource management in meat processing facilities. By integrating this innovative slicing technique, meat processors can expect an overall enhancement in product quality and resource utilization, ultimately leading to greater profitability and sustainability in their operations. This research opens avenues for further exploration and application of three-dimensional reconstruction technologies in various aspects of food processing and quality assurance.