门牙
发表于 2025-3-21 19:04:55
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发怨言
发表于 2025-3-22 00:00:45
978-3-030-89182-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
nauseate
发表于 2025-3-22 01:11:33
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装入胶囊
发表于 2025-3-22 06:37:12
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Narrative
发表于 2025-3-22 11:50:24
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变色龙
发表于 2025-3-22 15:06:55
https://doi.org/10.1007/978-3-642-88544-0and advanced driver assistance systems (ADAS). However, point cloud data is sparse, irregular, and unordered by nature. In addition, the sensor typically produces a large number (tens to hundreds of thousands) of raw data points, which brings new challenges, as many applications require real-time pr
defeatist
发表于 2025-3-22 20:03:50
,Die Flugeinheit und die Bodengeräte,ption. Since 2017, researchers have become inclined to train end-to-end networks for tasks like point cloud classification, semantic segmentation, and object detection. More recently, other tasks like registration and odometry have also been solved using Deep learning. These newer data-driven method
Militia
发表于 2025-3-22 23:34:23
https://doi.org/10.1007/978-3-642-88545-7their interpretation. These methods are an extension of successive subspace learning (SSL) from 2D images to 3D point clouds. SSL offers a lightweight unsupervised feature learning method based on the inherent statistical properties of data units. The model is significantly smaller than deep neural
使绝缘
发表于 2025-3-23 01:32:03
https://doi.org/10.1007/978-3-642-88545-7is more effective and efficient. It is common for new researchers to focus only on Deep learning methods while lacking a solid foundation of the fundamental knowledge of traditional methods. However, the traditional point cloud processing methods are the root of Deep learning methods, and they are s
漫步
发表于 2025-3-23 06:24:09
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