## 1 Introduction  
Three-dimensional (3D) target detection plays a crucial role in various scientific and engineering fields. With the development of 3D laser ranging technology, Time-of-Flight (ToF) cameras now have a higher ranging accuracy and greater resolution, which allows the users to collect three-dimentional point cloud data qualified to be applied on high-precision target detection tasks [1]. For researchers, it is necessary to measure the geometric characteristics such as length, thickness, and projection boundaries to analyze physical movements and deformation of a target, encompassing rigid ones such as projected area of structuresalong the flow direction [2; 3], and deformable ones such as parachute deployments [4; 5] or even the accumulation of an ice cluster. To calculate the geometric characteristics of a target, it is necessary to obtain their projection boundary in certain directions. For a parachute, as an example, the projection boundary is closely related to the wind resistance and is the key to judging whether the structure is fully deployed. However, raw point clouds from a ToF camera contain noise points, incomplete areas, and ranging errors caused by the inherent limitations of the sensors or the matching ambiguity in image reconstruction. These factors will seriously affect downstream tasks, especially measurements. Therefore, developing a coherent preprocessing flow for ToF cameras is the key to achieving an integration of real-time 3D measurement and data output. A traditional approach in engineering is to apply multiple existing methods to deal with these defects one by one, but this requires tuning and optimizing parameters for each model separately, and takes up a large amount of computing space. On the other hand, recent research has only focused on 3D de-noising or completion of rigid targets, and then using a very simple traditional method to extract the projection boundaries. These methods may not be applicable in practice because existing real scan datasets lack labels for the outlier points, while computer-simulated de-noising datasets are mostly generated from complete 3D models thus cannot simulate the original output of ToF cameras well. Since it is difficult to establish completion standards for deformable targets, rigid point cloud completion algorithms are also of little use. In addition, traditional 3D object projection boundary extraction methods also rely on parameter tuning and density consistency of the target point cloud. To solve the above problems, we propose a deep-learning-based point cloud processing flow called PcBD. The PcBD network contain three parts, each of which focuses on one processing task, and the three-dimensional features of the input point cloud are retained after each block and further enhanced in the next block to have stronger two-dimensional and three-dimensional representation capabilities. Figure 1 shows the processing flow of PcBD on a raw target point cloud of a ToF camera. In order to accurately recognize and remove outliers, we apply the SHOT local reference frame (LRF) construction [6] in the Feature-Extraction block and introduce an improved Point Transformer [7] to simultaneously process three-dimensional coordinates and local reference frames. In the Boundary-Detecting and the Smoothing blocks, we propose a Cross Transformer strcture to remind the three-dimensional features and the newly generated two-dimensional features of their positions in each other's geometric shapes, and further combine 3D structures and 2D shapes to achieve accurate detecting and smoothing of the projection boundary. We design a suitable method in the network structure to ensure that the total number of points remains unchanged while removing outliers and outputting the projection boundary, which is necessary for a point cloud processing flow. We also introduce a new benchmark Bound57 for point cloud processing flow, which simulates the raw output of ToF cameras from 3D objects through carefully designed methods.