Estimating the 6D object pose based on a monocular RGB image is a challenging task in computer vision, which produces false positives under the influence of occlusion or cluttered environments. In addition, the prediction of translation is affected by changes of the image size. In this work, we present a novel two-stage method TGCPose6D for robust 6DoF object pose estimation which is composed of 2D keypoint detection and translation refinement. In the first stage, the 2D keypoint regression space is constrained by triangulate geometric feature vectors, and the low-quality prediction is suppressed by the center-heatmap weighted loss function, thereby the performance of keypoint detection is significantly improved. In the second stage, the Visual-Flow Fusion network (VFFNet) is used to extract the visual feature and optical flow feature of the rendered image and the observed image, and to predict the relative translation based on the difference of features. Specifically, the VFFNet is trained iteratively to gain the ability to predict the relative translation deviation. Extensive experiments are conducted to demonstrate the effectiveness of the proposed TGCPose6D method. Our overall pose estimation pipeline outperforms state-of-the-art object pose estimation methods on several benchmarks.

在计算机视觉中，基于单眼RGB图像估计6D对象姿态是一项艰巨的任务，在遮挡或混乱环境的影响下，它会产生误报。另外，翻译的预测受图像尺寸的变化影响。在这项工作中，我们提出了一种用于鲁棒6DoF对象姿态估计的新颖的两阶段方法TGCPose6D，该方法由2D关键点检测和平移精化组成。在第一阶段，二维三角点回归空间受到三角几何特征向量的约束，并且通过中心热图加权损失函数抑制了劣质预测，从而大大提高了关键点检测的性能。在第二阶段 视觉流融合网络（VFFNet）用于提取渲染图像和观察图像的视觉特征和光流特征，并根据特征的差异预测相对平移。具体来说，对VFFNet进行迭代训练，以获得预测相对平移偏差的能力。进行了广泛的实验，以证明所提出的TGCPose6D方法的有效性。我们的总体姿态估计管线在几个基准上都优于最新的物体姿态估计方法。进行了广泛的实验，以证明所提出的TGCPose6D方法的有效性。我们的总体姿态估计管线在几个基准上都优于最新的物体姿态估计方法。进行了广泛的实验，以证明所提出的TGCPose6D方法的有效性。我们的总体姿态估计管线在几个基准上都优于最新的物体姿态估计方法。