CorNet：用于无需重新训练的新对象的6D姿态估计的通用3D角
CorNet: Generic 3D Corners for 6D Pose Estimation of New Objects without Retraining
摘要：
我们提出了一种新的方法来检测和彩色图像中的物体的三维姿态估计。它的主要贡献是它不需要任何训练阶段，也不需要新对象的数据，而最先进的方法通常需要数小时的训练时间和数百个训练注册图像。相反，我们的方法只依赖于对象的几何形状。我们的方法侧重于具有突出角落的对象，这涵盖了大量的工业对象。我们首先学习检测图像中各种形状的对象角，并通过使用一小组对象的训练图像来预测它们的3D姿态。为了检测给定图像中的新对象，我们首先从其CAD模型中识别其角点;我们还检测图像中可见的角点并预测其3D姿态。然后，我们引入了一个类似于RANSAC的算法，该算法通过将CAD模型上的角点与图像中检测到的角点进行匹配，来鲁棒有效地检测和估计对象的3D姿态。因为我们还估计图像中角的3D姿态，所以仅检测1个或2个角就足以估计对象的姿态，这使得该方法对遮挡具有鲁棒性。最后，我们依赖于利用对象的完整3D几何形状的最终检查，以防多个对象具有相同的角空间布置。我们的方法的优点使其特别适合工业环境，我们在具有挑战性的T-LESS数据集上展示了我们的方法。
英文摘要：
We present a novel approach to the detection and 3D pose estimation of objects in color images. Its main contribution is that it does not require any training phases nor data for new objects, while state-of-the-art methods typically require hours of training time and hundreds of training registered images. Instead, our method relies only on the objects' geometries. Our method focuses on objects with prominent corners, which covers a large number of industrial objects. We first learn to detect object corners of various shapes in images and also to predict their 3D poses, by using training images of a small set of objects. To detect a new object in a given image, we first identify its corners from its CAD model; we also detect the corners visible in the image and predict their 3D poses. We then introduce a RANSAC-like algorithm that robustly and efficiently detects and estimates the object's 3D pose by matching its corners on the CAD model with their detected counterparts in the image. Because we also estimate the 3D poses of the corners in the image, detecting only 1 or 2 corners is sufficient to estimate the pose of the object, which makes the approach robust to occlusions. We finally rely on a final check that exploits the full 3D geometry of the objects, in case multiple objects have the same corner spatial arrangement. The advantages of our approach make it particularly attractive for industrial contexts, and we demonstrate our approach on the challenging T-LESS dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
