# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 1 Introduction  
The accuracy of tread size is crucial for ensuring the high-quality production of subsequent finished tires. However, the industrial production line environment introduces various interferences that affect the accuracy of tread size detection after cutting. Factors such as production line debris, mechanical vibrations, and conveyor speed all contribute to potential measurement errors. Relying on manual empirical methods can easily be disrupted by these uncontrollable factors, leading to material waste, poor detection effectiveness, and decreased production efficiency [1]. Thus, optimizing the measurement techniques for tread size after cutting is of significant practical importance. Machine vision technology has evolved significantly over the past 60 years. Currently, depth measurement technology based on RGB-D data is not only an essential tool in computer vision but also a prominent research focus in point cloud processing applications. Depth cameras have been used in 3D applications, such as ranging and 3D reconstruction, for over a decade. The authors of [2] demonstrate the use of a mobile single Kinect to create indoor scenes with real-time multi-touch interaction capabilities. The authors of [3] establish that the scanning accuracy of depth cameras is inversely correlated with the scanning distance. However, the quality of 3D reconstruction is generally poor. To address this, ref. [4] invoked the implicit function of non-surface volume in the NeRF framework and proposed a new RGB-D camera pose relocation technique to achievehigh-quality, measurable 3D reconstruction. Additionally, ref. [5] proposes an external parameter calibration method based on a monocular laser scattering projection system, which effectively improves both the calibration efficiency and the depth reconstruction accuracy of the system. In the process of 3D reconstruction, errors and gaps often occur when reconstructing reflective and protruding objects. To address these issues, ref. [6] employs two depth cameras to simultaneously capture infrared images of objects. By fusing the parallax calculation results with the depth images, this approach repairs and refines voids in the depth map and compensates for the mutual interference from the infrared spot projections of the two cameras, thereby improving the reconstruction quality of photo-reflective anomalous objects. Additionally, ref. [7] proposes a new RGB-D saliency detection model, CAAI-Net, which utilizes a complementary attention mechanism and adaptive feature fusion to detect saliency in multimodal RGB-D images, effectively overcoming the limitations of existing methods for accurately detecting salient objects. Although the quality of 3D reconstruction has improved, acquiring target data typically requires expensive equipment. To reduce data acquisition costs, ref. [8] proposes a voxel-based pipeline that employs voxel cropping operations, removes redundant voxels, and fills holes on surfaces. This method achieves better results in the complete and accurate reconstruction of sparse RGB-D image geometric models and texture mapping, offering greater applicability and flexibility for various conditions and multiple RGB-D camera devices. To further reduce the 3D reconstruction errors, the researchers introduced a point cloud alignment method. The authors of [9] present an object alignment method that combines the LK optical flow algorithm with the ICP point cloud alignment algorithm. This approach constructs an enhanced assembly guidance system for complex products. Using the engine model and complex weapon compartment equipment as examples, the experimental results demonstrate that the proposed alignment method is more accurate and stable. Point cloud methods are utilized not only in the industrial sector but also in the agricultural sector. In agriculture, the size of plant crown volume is closely related to yield. To achieve an accurate prediction of the crown volume of orange trees, ref. [10] proposes a dynamic slicing and reconstruction algorithm using 3D point clouds.