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.

翻译：对象识别和 6DoF 表示估算是计算机视觉应用中相当具有挑战性的任务。 尽管这些任务效率很高, 标准方法远非实时处理率。 本文展示了一个新的管道, 用于估算6DoF型物体的精细配置, 适用于实时现实情景。 我们将我们的提案分为三个主要部分。 首先, 色彩特征分类将利用在图像网络上受过培训的经过培训的CNN彩色功能进行目标探测。 基于特性的注册模块进行粗略的估算, 最后, 以精确调整步骤进行基于比较方案的密集登记。 我们的提案在最佳情况下, 实现了RGB- D Scenes数据集上近83 ⁇ 的准确性能。 关于处理时间, 目标检测任务按90 FPS 框架处理率完成, 并在全面执行战略中按近14 FPS 进行估计。 我们讨论的是,由于提案的模块性, 我们只能在必要的时候才能让全部执行, 并且执行一个能够解开实时处理( 即使是多任务情况下) 。