Abstract
Conventional point cloud data augmentation methods typically employ offline transformations with predefined, randomly applied transformations. This randomness may lead to suboptimal training samples that are not suitable for the current training stage. Additionally, the predefined parameter range restricts the exploration space of augmentation, limiting the diversity of samples. This paper introduces Degree-Accumulated Data Augmentation (\(\textrm{DA}^2\)), a novel approach that accumulates augmentations to expand the exploration space beyond predefined limits. We utilize a teacher-guided auto-augmenter to prevent the generation of excessively distorted or unrecognizable samples. This method aims to generate challenging yet suitable samples, progressively increasing the difficulty to enhance the model’s robustness. Additionally, according to a student model’s ability, we propose Curriculum Dynamic Threshold Selection (CDTS) to filter overly challenging samples, allowing the model to start with high-quality objects and gradually handle more complex ones as model stability improves. Our experiments show that this framework significantly enhances accuracy across various 3D point cloud classifiers.
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Acknowledgements
This work was financially supported in part (project number: 112UA10019) by the Co-creation Platform of the Industry Academia Innovation School, NYCU, under the framework of the National Key Fields Industry-University Cooperation and Skilled Personnel Training Act, from the Ministry of Education (MOE) and industry partners in Taiwan. It is also supported in part by the National Science and Technology Council, Taiwan, under Grant NSTC-112-2221-E-A49-089-MY3, Grant NSTC-110-2221-E-A49-066-MY3, Grant NSTC-111- 2634-F-A49-010, Grant NSTC-112-2425-H-A49-001, and in part by the Higher Education Sprout Project of the National Yang Ming Chiao Tung University and the Ministry of Education (MOE), Taiwan. We sincerely thank Hon Hai Research Institute for their invaluable support.
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Tai, T.C., Do-Tran, NT., Le, NHL., Li, YH., Huang, CC. (2025). DA\(^2\): Degree-Accumulated Data Augmentation on Point Clouds with Curriculum Dynamic Threshold Selection. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15480. Springer, Singapore. https://doi.org/10.1007/978-981-96-0969-7_1
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