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Deep learning for 3D object recognition: A survey
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DOI：10.1016/j.neucom.2024.128436.png)
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摘要
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With the growing availability of extensive 3D datasets and the rapid progress in computational power, deep learning (DL) has emerged as a highly promising approach for learning from 3D data, addressing critical tasks like object detection, segmentation, and recognition. Despite the unique challenges in processing geometry data with deep neural networks, recent advancements in DL for 3D object recognition have shown remarkable success, with various methods proposed to tackle different issues. This paper aims to stimulate future research by providing a comprehensive review of recent progress in DL techniques for 3D object recognition, which are systematically categorized based on their learning behavior. We discuss the advantages, limitations, and application of each approach, highlighting their performance in 3D object classification on benchmark datasets such as ModelNet, ScanObjectNN, and Sydney Urban Object. The survey offers insightful observations and inspires future research directions.
Keyword:
3D shape analysis
3D object classification
Point cloud classification
Volumetric CNN
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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