Abstract The task of detecting 3D objects is important in various robotic applications. The existing deep learning-based detection techniques have achieved impressive performances. However, these techniques are limited to being run on a graphics processing unit (GPU) in a real-time environment. To achieve real-time 3D object detection with limited computational resources, we propose an efficient detection method based on 3D proposal generation and classification. The proposal generation is based mainly on point segmentation, while proposal classification is performed by a lightweight convolution neural network (CNN). KITTI datasets are then used to validate our method. It takes only 0.082 s for our method to process one point block with one core of the central processing unit (CPU). In addition to efficiency, the experimental results also demonstrate the capability of the proposed method of producing a competitive performance in object recall and classification.

中文翻译：

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使用有限的处理资源进行实时 3D 对象提议生成和分类

摘要 检测 3D 对象的任务在各种机器人应用中都很重要。现有的基于深度学习的检测技术已经取得了令人印象深刻的性能。然而，这些技术仅限于在实时环境中的图形处理单元 (GPU) 上运行。为了在有限的计算资源下实现实时 3D 对象检测，我们提出了一种基于 3D 提议生成和分类的有效检测方法。提议生成主要基于点分割，而提议分类由轻量级卷积神经网络 (CNN) 执行。然后使用 KITTI 数据集来验证我们的方法。我们的方法只需要 0.082 s 就可以用一个中央处理单元 (CPU) 的一个核心处理一个点块。除了效率，