## 2 Related Work  
**Point cloud de-noising. ** Real scans of a ToF camera can contain two kinds of noise: systematic ranging errors, and the spatial outlier points which could be caused by tiny ash or rain drops in the air, or entirely due to the target reflectivity and reflection angle, especially when there is an existence of mirror-like surfaces. Early methods made approaches to filter out the outlier points with lower neighbor density [8, 9], these methods usually rely on prior data, and could only remove the outlier noisy points. Other researchers have made approaches to use a bilateral filter [10, 11] or to approximate and project points onto an underlying surface [12] to deal with the underlying noise of each point, while flexible objects often cannot be represented as a set of regular surfaces, and the bilateral filters would wipe out the edge areas of low density. Some graph-based methods have introduced graph filters to graph-represented point clouds [13, 14], but the 3D representation accuracy of these methods is seriously insufficient. It is becoming clearer that a more proper way to process 3D point clouds is to operate on each single point, and methods have been proposed to deal with 3D coordinates, such as PointNet++ [15] and other point cloud feature encoders based on convolutional neural network layers [16, 17, 18]. Some deep-learning-based methods focus on outlier removal using spatial and temporal information [19, 20, 21], which have been proved to be efficient on processing weather-caused noise points, but the effect on ranging noise is limited. PCNet [22] firstly came up with an idea to predict displacement for each point, and other methods followed to optimize the reasoning ability of the network to the underlying surface [23, 24, 25]. The method proposed in [26] employed score matching [27] to estimate the noise-convolved distribution of a noisy point cloud and de-noise the point cloud by gradient ascent, and has been proved to be able to be applied on arbitrary shapes. [PERSON] et al. introduced P2P-Bridge to treat de-noising as finding an optimal transport (Schrodinger bridge) between noisy and clean point clouds, and achieved significant improvement over prior methods on existing datasets [28]. These methods do not distinguish between outliers and ranging errors and discuss how to deal with them separately. Some recent studies have simultaneously implemented outlier filtering and point cloud de-noising in a single network [29], but their training data is only generated from complete 3D models by down-sampling and adding simple Gaussian noise. None of these above-mentioned methods form a stable processing flow of depth camera output data. The PointCVaR [30] was proposed to de-noise the input point clouds based on the results of classification networks. However, it only relies on the prediction output of other systems and does not utilize the effective three-dimensional features extracted by the preceding system. **Point cloud boundary detecting. ** The methods to determine the boundary or a \"hull\" of a projected point cloud can be separated to convex hulls and concave hulls, which are mainly named after the shape of the constructed hull. [PERSON] et al. [31] proposed a gift-wrapping algorithm to generate a convex hull for point clouds in arbitrary dimensional space. The [PERSON]'s algorithm [32] proposed in 1972 aimed to judge whether a point is a vertex on the convex hull by calculating the cross product of vectors between three points. Nevertheless, a convex hull is not perfectly suitable for a flexible target because the boundary of the projected cloud is not likely to be a convex polygon. The alpha-shape method [33] was proposed to solve such problems as it constructs a concave hull by rolling a circle with a fixed radius around the cloud to detect the edge points. Several other approaches have been introduced to improve parameter adjustments [34, 35].