State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel). 2021 Feb 6;21(4):1141. doi: 10.3390/s21041141.
Planar surfaces are prevalent components of man-made indoor scenes, and plane extraction plays a vital role in practical applications of computer vision and robotics, such as scene understanding, and mobile manipulation. Nowadays, most plane extraction methods are based on reconstruction of the scene. In this paper, plane representation is formulated in inverse-depth images. Based on this representation, we explored the potential to extract planes in images directly. A fast plane extraction approach, which employs the region growing algorithm in inverse-depth images, is presented. This approach consists of two main components: seeding, and region growing. In the seeding component, seeds are carefully selected locally in grid cells to improve exploration efficiency. After seeding, each seed begins to grow into a continuous plane in succession. Both greedy policy and a normal coherence check are employed to find boundaries accurately. During growth, neighbor coplanar planes are checked and merged to overcome the over-segmentation problem. Through experiments on public datasets and generated saw-tooth images, the proposed approach achieves 80.2% CDR (Correct Detection Rate) on the ABW SegComp Dataset, which has proven that it has comparable performance with the state-of-the-art. The proposed approach runs at 5 Hz on typical 680 × 480 images, which has shown its potential in real-time practical applications in computer vision and robotics with further improvement.
平面是人造室内场景中常见的组成部分，平面提取在计算机视觉和机器人技术的实际应用中起着至关重要的作用，例如场景理解和移动操作。如今，大多数平面提取方法都是基于场景的重建。在本文中，我们在逆深度图像中对平面表示进行了公式化。基于这种表示，我们探讨了直接从图像中提取平面的潜力。提出了一种基于逆深度图像的区域生长算法的快速平面提取方法。该方法主要包括两个部分：种子生成和区域生长。在种子生成部分，通过在网格单元中进行局部精细选择来提高探索效率。种子生成后，每个种子开始依次连续生长为一个连续的平面。采用贪婪策略和正常一致性检查来准确找到边界。在生长过程中，检查并合并邻近平行平面，以克服过分割问题。通过在公共数据集和生成的锯齿形图像上的实验，该方法在 ABW SegComp 数据集上的 CDR（正确检测率）达到 80.2%，这证明了它与最先进方法的性能相当。该方法在典型的 680×480 图像上的运行速度为 5Hz，这表明它在计算机视觉和机器人技术的实时实际应用中具有潜力，并且可以进一步改进。
