论文标题
实时3D对象提案生成和分类有限的处理资源
Real-time 3D object proposal generation and classification under limited processing resources
论文作者
论文摘要
检测3D对象的任务对于各种机器人应用很重要。现有的基于深度学习的检测技术已取得了令人印象深刻的性能。但是，这些技术仅限于在实时环境中使用图形处理单元（GPU）运行。为了在机器人的计算资源有限的情况下实现实时3D对象检测，我们提出了一种由3D建议生成和分类组成的有效检测方法。提案生成主要基于点细分，而建议分类由轻量级卷积神经网络（CNN）模型执行。为了验证我们的方法，使用Kitti数据集。实验结果证明了从点云中提出的实时3D对象检测方法的能力，具有对象回忆和分类的竞争性能。
The task of detecting 3D objects is important to various robotic applications. The existing deep learning-based detection techniques have achieved impressive performance. However, these techniques are limited to run with a graphics processing unit (GPU) in a real-time environment. To achieve real-time 3D object detection with limited computational resources for robots, we propose an efficient detection method consisting of 3D proposal generation and classification. The proposal generation is mainly based on point segmentation, while the proposal classification is performed by a lightweight convolution neural network (CNN) model. To validate our method, KITTI datasets are utilized. The experimental results demonstrate the capability of proposed real-time 3D object detection method from the point cloud with a competitive performance of object recall and classification.
