Advancing Trans-Domain Classification With Knowledge Distillation: Bridging LIDAR and Image Data

作     者：Ortiz, Jesus Eduardo Creixell, Werner 

作者机构：Univ Tecn Federico Santa Maria Dept Elect Valparaiso 2390123 Chile 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：20574-20583页

核心收录：

基　　金：Technical University Federico Santa Maria 

主　　题：Computational modeling Adaptation models Data models Laser radar Biological system modeling Transformers Sensors Point cloud compression Knowledge engineering Image classification Knowledge distillation trans-domain classification point cloud classification LIDAR data processing deep learning model compression sensor fusion efficient machine learning autonomous systems cross-domain machine learning 

摘      要：Recent advancements in deep learning have significantly improved image classification models, yet extending these models to alternative data forms, such as point clouds from Light Detection and Ranging (LiDAR) sensors, presents considerable challenges. This paper explores applying knowledge distillation techniques as a solution, aiming to transfer the learned competencies from established image classification frameworks to point cloud classification tasks. Our methodology involves distilling complex model insights into more computationally efficient forms suitable for LIDAR data, thus enabling substantial resource savings without sacrificing performance. Experimental evaluations across various benchmark datasets demonstrate that our distilled models not only rival their original counterparts in accuracy but also surpass conventional point cloud classification methods in both efficiency and scalability. Additionally, we delve into the impact of varying distillation techniques on model adaptability and performance within the LIDAR domain. The findings underscore the utility of knowledge distillation in enhancing the trans-domain applicability of image classification models, potentially revolutionizing their deployment across diverse data types.