Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor
Three-dimensional point cloud recognition is a very fundamental work in fields such as autonomous driving and face recognition. However, in real industrial scenarios, input point cloud data are often accompanied by factors such as occlusion, rotation, and noise. These factors make it challenging to...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7749 |
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| author | Xian Wu Xueyi Guo Hang Peng Bin Su Sabbir Ahamod Fenglin Han |
| author_facet | Xian Wu Xueyi Guo Hang Peng Bin Su Sabbir Ahamod Fenglin Han |
| author_sort | Xian Wu |
| collection | DOAJ |
| description | Three-dimensional point cloud recognition is a very fundamental work in fields such as autonomous driving and face recognition. However, in real industrial scenarios, input point cloud data are often accompanied by factors such as occlusion, rotation, and noise. These factors make it challenging to apply existing point cloud classification algorithms in real industrial scenarios. Currently, most studies enhance model robustness from the perspective of neural network structure. However, researchers have found that simply adjusting the neural network structure has proven insufficient in addressing the decline in accuracy caused by data corruption. In this article, we use local feature descriptors as a preprocessing method to extract features from point cloud data and propose a new neural network architecture aligned with these local features, effectively enhancing performance even in extreme cases of data corruption. In addition, we conducted data augmentation to the 10 intentionally selected categories in ModelNet40. Finally, we conducted multiple experiments, including testing the robustness of the model to occlusion and coordinate transformation and then comparing the model with existing SOTA models. Furthermore, in actual scene experiments, we used depth cameras to capture objects and input the obtained data into the established model. The experimental results show that our model outperforms existing popular algorithms when dealing with corrupted point cloud data. Even when the input point cloud data are affected by occlusion or coordinate transformation, our proposed model can maintain high accuracy. This suggests that our method can alleviate the problem of decreased model accuracy caused by the aforementioned factors. |
| format | Article |
| id | doaj-art-321d3b5450c74ce5b4a32443dfd3aeb7 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-321d3b5450c74ce5b4a32443dfd3aeb72024-12-13T16:32:39ZengMDPI AGSensors1424-82202024-12-012423774910.3390/s24237749Corrupted Point Cloud Classification Through Deep Learning with Local Feature DescriptorXian Wu0Xueyi Guo1Hang Peng2Bin Su3Sabbir Ahamod4Fenglin Han5School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaResource Recycling Research Institute, Central South University, Changsha 410083, ChinaSchool of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaSchool of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaSchool of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaSchool of Mechanical and Electrical Engineering, Central South University, Changsha 410083, ChinaThree-dimensional point cloud recognition is a very fundamental work in fields such as autonomous driving and face recognition. However, in real industrial scenarios, input point cloud data are often accompanied by factors such as occlusion, rotation, and noise. These factors make it challenging to apply existing point cloud classification algorithms in real industrial scenarios. Currently, most studies enhance model robustness from the perspective of neural network structure. However, researchers have found that simply adjusting the neural network structure has proven insufficient in addressing the decline in accuracy caused by data corruption. In this article, we use local feature descriptors as a preprocessing method to extract features from point cloud data and propose a new neural network architecture aligned with these local features, effectively enhancing performance even in extreme cases of data corruption. In addition, we conducted data augmentation to the 10 intentionally selected categories in ModelNet40. Finally, we conducted multiple experiments, including testing the robustness of the model to occlusion and coordinate transformation and then comparing the model with existing SOTA models. Furthermore, in actual scene experiments, we used depth cameras to capture objects and input the obtained data into the established model. The experimental results show that our model outperforms existing popular algorithms when dealing with corrupted point cloud data. Even when the input point cloud data are affected by occlusion or coordinate transformation, our proposed model can maintain high accuracy. This suggests that our method can alleviate the problem of decreased model accuracy caused by the aforementioned factors.https://www.mdpi.com/1424-8220/24/23/7749deep neural networkslocal feature descriptorobject classificationpoint cloudpartial point cloud |
| spellingShingle | Xian Wu Xueyi Guo Hang Peng Bin Su Sabbir Ahamod Fenglin Han Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor Sensors deep neural networks local feature descriptor object classification point cloud partial point cloud |
| title | Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor |
| title_full | Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor |
| title_fullStr | Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor |
| title_full_unstemmed | Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor |
| title_short | Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor |
| title_sort | corrupted point cloud classification through deep learning with local feature descriptor |
| topic | deep neural networks local feature descriptor object classification point cloud partial point cloud |
| url | https://www.mdpi.com/1424-8220/24/23/7749 |
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