# R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption
## 1 Introduction  
Point cloud refers to a collection of points that represent the spatial distribution and surface characteristics of targets in three-dimensional space. It is commonly used to represent three-dimensional data and contains rich information such as XYZ coordinates, color, intensity value, and time [1]. Point clouds can be obtained using 3D scanners or generated through computer-aided design (CAD) models. With the increasing popularity of 3D data acquisition devices, point clouds have garnered significant attention in various fields including object detection, autonomous driving [2, 3], and robotics [4, 5]. However, due to the factors such as equipment accuracy and environmental conditions, point clouds obtained from realistic scenes using 3D scanners often contain corruption, including noise and outliers. Figure 1 shows an example of a raw real-world scanned point cloud. In real-world applications, the raw point cloud is usually cleaned up by discarding outliers and denoising the remaining points prior to point cloud recognition, in order to enhance recognition accuracy. Traditional methods for clearing raw point cloud corruption have been researched in numerous cases [6], such as invalid value removal, statistical approaches [7], radius filtering methods, and so forth. Expectedly, no single traditional method for filtering raw point cloud corruption dominates in the recognition task of point clouds. The choice of algorithm typically relies on the specific circumstances of the recognition application or the equipment used for data acquisition. Furthermore, the selection of the appropriate parameters for cleaning point cloud corruption is contingent upon the scene's characteristics, the data inquestion, and the algorithm employed. Consequently, the performance of these methods heavily relies on the experience and adaptability of the algorithm to the application. Since the emergence of deep learning, various deep 3D point cloud recognition architectures have been proposed. Typically, there are three approaches for recognition. The first approach is based on 3D voxel, which convert spatially distributed cluttered point clouds into 3D voxel grids and then recognize them by a well-established 2D or 3D convolutional network. The second approach focuses on multi-view projection images, which project unstructured point clouds into 2D images for classification; and the third utilize point clouds directly. However, both the first and second approaches suffer from the drawback of losing original information through the transformation of point cloud formats. The pioneering work, PointNet [8], addresses this issue by directly operating on raw point clouds and capturing global features for recognition. Furthermore, in order to improve the recognition accuracy and capture local features of the data, PointNet++ [9] was proposed. However, increasing model complexity results in a slight decrease in the robustness of the network when dealing with the corruption of raw point clouds in realistic scenarios. In this paper, we propose a robust end-to-end point cloud recognition network, named R-PointNet. Our network can capture local discrepancy features through the compensation module, offset-adjustment (OA), and effectively handle noise and outliers in raw point clouds. Among the various methods used for point cloud recognition, ModelNet40 [10] is considered the most widely accepted benchmark. Over the years, there have been continuous improvements and innovations in network models, leading to enhanced recognition accuracy on ModelNet40. However, these advancements only evaluate model performance from a single perspective using clean data. Given the complexity and significance of 3D point clouds in real-world application, it is crucial to conduct comprehensive robustness benchmarking of point cloud recognition models. In order to better approximate the deficiencies of real-world scanned point clouds and more accurately evaluate our network, we tested R-PointNet on the ModelNet40-C dataset.