Many edge applications, such as collaborative robotics and spacecraft rendezvous, can benefit from 6D object pose estimation, but must do so on embedded platforms. Unfortunately, existing 6D pose estimation networks are typically too large for deployment in such situations and must therefore be compressed, while maintaining reliable performance. In this work, we present an approach to doing so by quantizing such networks. More precisely, we introduce a module-wise quantization strategy that, in contrast to uniform and mixed-precision quantization, accounts for the modular structure of typical 6D pose estimation frameworks. We demonstrate that uniquely compressing these modules outperforms uniform and mixed-precision quantization techniques. Moreover, our experiments evidence that module-wise quantization can lead to a significant accuracy boost. We showcase the generality of our approach using different datasets, quantization methodologies, and network architectures, including the recent ZebraPose.

翻译：许多边缘应用，例如协作机器人和航天器交会，可以从6D物体姿态估计中受益，但必须在嵌入式平台上实现。然而，现有的6D姿态估计网络通常过大，难以在此类场景中部署，因此必须在保持可靠性能的同时进行压缩。本文提出了一种通过量化此类网络来实现压缩的方法。具体而言，我们引入了一种模块级量化策略——与均匀量化和混合精度量化不同——该策略考虑了典型6D姿态估计框架的模块化结构。我们证明，对各个模块进行独立压缩优于均匀量化和混合精度量化技术。此外，我们的实验表明，模块级量化可以显著提升精度。我们使用不同数据集、量化方法和网络架构（包括最新的ZebraPose）展示了该方法的通用性。