| 利用多任务级联网络解决6D Pose预测问题 |
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| 作者姓名： | 刘进 赵帆 |
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| 作者单位： | 武汉大学测绘遥感信息工程国家重点实验室 ,湖北 武汉 ,430079 |
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| 基金项目： | 国家自然科学基金项目(41271454)。 |
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| 摘 要： | 针对基于单张RGB(red-green-blue)图像预测目标6D Pose的问题,设计了多任务级联结构的卷积神经网络(convolutional neural networks,CNN)和BBE(bounding box equation)算法实现快速高效的6D Pose预测。在LINEMOD数据集上进行实验,并与LINE2D和Brachmann预测算法进行比较,结果表明,该方法速度和精度均超过LINE2D算法,精度上接近Brachmann算法,但速度更快。
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| 关 键 词： | 卷积神经网络 多任务级联结构 6D Pose预测 |
Application of Multi-task Cascade Neural Network in 6D Pose Prediction Problem |
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| Authors: | LIU Jin ZHAO Fan |
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| Institution: | (State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China) |
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| Abstract: | Aiming at the problem of predicting 6 D Pose based on single RGB image,a multi-task cascade convolutional neural network and Bounding Box Equation algorithm are designed to realize fast and efficient 6 D Pose prediction.LINEMOD dataset is used and LINE2 D and Brachmann prediction algorithm are compared-in the experiment.The result shows that the speed and accuracy of the proposed method exceed the LINE2 D algorithm,and the accuracy is close to the Brachmann algorithm,but the speed is faster. |
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| Keywords: | convolutional neural network multi-task cascade structure 6D Pose prediction |
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