论文标题
使用多深度相机系统的扫描动物的语义关键点提取
Semantic keypoint extraction for scanned animals using multi-depth-camera systems
论文作者
论文摘要
点云中的关键点注释是3D重建，对象跟踪和对齐的重要任务，尤其是在可变形或移动场景中。在农业机器人技术的背景下，牲畜自动化是朝着疾病评估或行为识别识别的关键任务。在这项工作中，我们通过将关键点提取作为关键点的回归问题与关键点与点云的其余部分之间的回归问题进行重新调整，以对点云中的语义关键点注释提出一种新颖的方法。我们使用映射到径向基础函数（RBF）的点云流形上的距离，然后使用编码器解码器体系结构来学习。通过考虑外部校准和相机框架辍学的噪声，对多深度相机系统的数据增强进行了特殊考虑。此外，我们研究了可以应用于动物点云的计算有效的非刚性变形方法。我们的方法对在现场，移动肉牛收集的数据进行了测试，该牛肉的校准系统由多个硬件同步的RGB-D摄像机进行校准。
Keypoint annotation in point clouds is an important task for 3D reconstruction, object tracking and alignment, in particular in deformable or moving scenes. In the context of agriculture robotics, it is a critical task for livestock automation to work toward condition assessment or behaviour recognition. In this work, we propose a novel approach for semantic keypoint annotation in point clouds, by reformulating the keypoint extraction as a regression problem of the distance between the keypoints and the rest of the point cloud. We use the distance on the point cloud manifold mapped into a radial basis function (RBF), which is then learned using an encoder-decoder architecture. Special consideration is given to the data augmentation specific to multi-depth-camera systems by considering noise over the extrinsic calibration and camera frame dropout. Additionally, we investigate computationally efficient non-rigid deformation methods that can be applied to animal point clouds. Our method is tested on data collected in the field, on moving beef cattle, with a calibrated system of multiple hardware-synchronised RGB-D cameras.
