We propose an approach for recognizing the pose and surface material of diverse objects,leveraging diffuse reflection principles and data fusion.Through theoretical analysis and the derivation of factors influencing d...We propose an approach for recognizing the pose and surface material of diverse objects,leveraging diffuse reflection principles and data fusion.Through theoretical analysis and the derivation of factors influencing diffuse reflection on objects,the method concentrates on and exploits surface information.To validate the feasibility of our theoretical research,the depth and active infrared intensity data obtained from a single time-of-flight camera are initially combined.Subsequently,these data undergo processing using feature extraction and lightweight machine-learning techniques.In addition,an optimization method is introduced to enhance the fitting of intensity.The experimental results not only visually showcase the effectiveness of our proposed method in accurately detecting the positions and surface materials of targets with varying sizes and spatial locations but also reveal that the vast majority of the sample data can achieve a recognition accuracy of 94.8%or higher.展开更多
基金supported by the Shaanxi Province Innovation Talent Promotion Program-Science and Technology Innovation Team(Grant No.2023-CX-TD-03).
文摘We propose an approach for recognizing the pose and surface material of diverse objects,leveraging diffuse reflection principles and data fusion.Through theoretical analysis and the derivation of factors influencing diffuse reflection on objects,the method concentrates on and exploits surface information.To validate the feasibility of our theoretical research,the depth and active infrared intensity data obtained from a single time-of-flight camera are initially combined.Subsequently,these data undergo processing using feature extraction and lightweight machine-learning techniques.In addition,an optimization method is introduced to enhance the fitting of intensity.The experimental results not only visually showcase the effectiveness of our proposed method in accurately detecting the positions and surface materials of targets with varying sizes and spatial locations but also reveal that the vast majority of the sample data can achieve a recognition accuracy of 94.8%or higher.
