# R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption
## 5 Conclusions  
Compared to clean datasets, point cloud data in real-world scenarios often contain corruption from noise and outliers. In the field of 3D object recognition, existing networks largely focus on improving the accuracy of clean datasets. Although they have reported promising accuracy on such datasets, their performance is still unsatisfactory in practical application deployment. Hence, object recognition in raw point clouds remains an extremely challenging task.  
In this paper, we propose the offset-adjustment (OA) module and build an end-to-end network R-PointNet to deal with real-world datasets. The OA module benefits the feature learning of raw point clouds by adjusting the spatial distribution of sampled defective  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Ablation** & **Accuracy (M40)** & **Accuracy (M40C)** \\ \hline A & PN only & 90.6 & 86.9 \\ B & PN+kNN & 90.1 & 87.2 \\ C & PN+kNN+OA & **91.9** & **90.0** \\ D & DGCNN & 92.2 & 84.6 \\ E & DGCNN+OA & 92.5 & 88.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study on the ModelNet40 (M40) and ModelNet40-C (M40C) validation set. PN, kNN, and OA mean PointNet++ architecture, K-nearest neighbor point sampling and offset-adjustment module.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multicolumn{6}{c}{**Accuracy (M40C)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Density Corruption**} & \multicolumn{3}{c}{**Nosie Corruption**} & \multicolumn{3}{c}{**Transformation**} \\  & \multicolumn{2}{c}{**Corruption**} & \multicolumn{2}{c}{**Corruption**} \\ \cline{2-9}  & **Occlusion** & **LiDAR** & **Uniform** & **Gaussian** & **Impulse** & **Upsampling** & **Background** & **Rotation** \\ \hline PointNet & 58.2 & **55.6** & 90.7 & 89.6 & 81.4 & 89.0 & 16.5 & 73.6 \\ RSCNN & 58.7 & 42.1 & 85.9 & 86.7 & 64.2 & 90.3 & **92.5** & 81.3 \\ DGCNN & 51.3 & 29.6 & 88.5 & 87.0 & 85.6 & 91.4 & 57.4 & 90.2 \\ PCT & 53.9 & 33.8 & 93.5 & 92.1 & **85.9** & **93.1** & 52.6 & **92.1** \\ PointNet++ & 55.8 & 45.0 & 87.6 & 86.4 & 75.4 & 85.7 & 82.4 & 82.9 \\ R-PointNet & **59.2** & 50.4 & **94.1** & **93.4** & 76.1 & 90.1 & 86.1 & 86.1 \\ Average & 56.2 & 42.8 & 90.1 & 89.2 & 78.1 & 89.9 & 64.6 & 84.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of different model architectures on ModelNet40-C (M40C) datasets with different corruption.  
points. The adjusted point cloud model aligns more accurately with the intrinsic geometry of the point cloud model. R-PointNet is evaluated on synthetic and real-world datasets through systematic benchmarking and analysis. In the experiments, its accuracy reaches 92.5% on the clean dataset ModelNet40, which is higher than almost any other point cloud recognition network. Additionally, our network outperforms previous networks by 3-7.7% on the corruption dataset ModelNet40-C. The experiments demonstrate that the OA module reduces the effect of erroneous and extraneous points in the sampling and enables the model to learn more comprehensive global features to enhance network robustness.  
Our analysis in this work is limited to point cloud recognition, which is an important problem in 3D scene understanding and is an essential component of object detection and retrieval systems. In future work, we intend to apply the methods of this paper to point cloud segmentation to improve the robustness of network in real-world applications.  
Conceptualization, Z.Z.; Methodology, Z.Z.; Validation, Z.Z.; Investigation, L.L.; Writing--original draft, Z.Z.; Writing--review & editing, X.Z.; Supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by Shanghai Science and Technology Committee under grant No. 22511106000.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.