论文标题
在点云中进行实时对象识别和姿势估计
Towards real-time object recognition and pose estimation in point clouds
论文作者
论文摘要
对象识别和6DOF姿势估计是计算机视觉应用程序中非常具有挑战性的任务。尽管在此类任务上有效率，但标准方法远非实时处理率。本文提出了一条新型的管道，以估算一个精细的6DOF姿势，并实时应用于现实的场景。我们将提议分为三个主要部分。首先，颜色特征分类利用了在成像网上训练的预训练的CNN颜色特征进行对象检测。一个基于特征的注册模块进行了粗糙的姿势估计，最后，调整良好的调整步骤执行了基于ICP的密集登记。在最好的情况下，我们的建议在RGB-D场景数据集中实现了几乎83 \％的准确性性能。关于处理时间，对象检测任务以高达90 fps的帧处理速率完成，并且在完整的执行策略中以接近14 fps的姿势估算。我们讨论，由于提案的模块化，我们只能在必要时才进行完整执行，并执行安排的执行，即使在多任务情况下，也可以解锁实时处理。
Object recognition and 6DoF pose estimation are quite challenging tasks in computer vision applications. Despite efficiency in such tasks, standard methods deliver far from real-time processing rates. This paper presents a novel pipeline to estimate a fine 6DoF pose of objects, applied to realistic scenarios in real-time. We split our proposal into three main parts. Firstly, a Color feature classification leverages the use of pre-trained CNN color features trained on the ImageNet for object detection. A Feature-based registration module conducts a coarse pose estimation, and finally, a Fine-adjustment step performs an ICP-based dense registration. Our proposal achieves, in the best case, an accuracy performance of almost 83\% on the RGB-D Scenes dataset. Regarding processing time, the object detection task is done at a frame processing rate up to 90 FPS, and the pose estimation at almost 14 FPS in a full execution strategy. We discuss that due to the proposal's modularity, we could let the full execution occurs only when necessary and perform a scheduled execution that unlocks real-time processing, even for multitask situations.
