基于上下文聚类融合的单视图整体三维理解算法研究

单视图整体三维理解，即从单张 RGB-D 图像中检测多个目标物体并推断其六自由度位姿、三维形状以及真实尺寸，在机器人作业、自动驾驶、虚拟现实等领域具有重要意义。因为依赖先验信息的传统算法不能匹配未见过的感兴趣物体，基于深度学习的解决方案已成为研究热点。目前，关于单视图整体三维理解的工作主要分为两类：先从图像中分割出目标实例区域的多阶段方案和直接推断多目标三维信息的单阶段方案。前者计算成本高，且严重依赖图像分割质量，在复杂的多目标遮挡场景表现不佳；后者不能很好地处理目标物体类内差异性，也缺乏对三维空间结构的理解，在识别和定位方面性能较差。

为了解决单阶段方案存在的问题，本论文设计了基于上下文聚类融合的单视图整体三维理解算法 CoCFusion  (Context Cluster Fusion)。与现有研究的主要差异如下：(1)提出坐标系分离的输入端处理方法，能够更加显式地挖掘真实空间几何 信息并提高网络对形状信息分布的理解力。(2)构建由上下文聚类模块和空间-通道注意力模块组成的层次化特征融合网络以及改进的点云聚类自编码器，以相似性度量的方式分层聚合不同偏好的特征，使网络更关注簇间整体差异而不是外观和形状上的细节匹配。(3)引入置信度几何一致性约束，增强网络对二维像素平面与真实三维空间映射过程的学习能力。

本论文在 NOCS 数据集上进行了验证。实验结果表明，CoCFusion 的性能显著优于现有单阶段整体三维理解算法，对于未见过的目标实例，六自由度位姿估计的类平均精度最高取得了 8.7% 的绝对提升。在华为技术有限公司 2012 实验室的支持下，本论文搭建了精度为 0. 1 mm 的机械臂视觉伺服实验系统，在实际工业生产场景中以 35 FPS 的速率成功完成算法测试,证明研究内容具有一定实用价值。

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