Edge applications, such as collaborative robotics and spacecraft rendezvous, demand efficient 6D object pose estimation on resource-constrained embedded platforms. Existing 6D pose estimation networks are often too large for such deployments, necessitating compression while maintaining reliable performance. To address this challenge, we introduce Modular Quantization-Aware Training (MQAT), an adaptive and mixed-precision quantization-aware training strategy that exploits the modular structure of modern 6D pose estimation architectures. MQAT guides a systematic gradated modular quantization sequence and determines module-specific bit precisions, leading to quantized models that outperform those produced by state-of-the-art uniform and mixed-precision quantization techniques. Our experiments showcase the generality of MQAT across datasets, architectures, and quantization algorithms. Remarkably, MQAT-trained quantized models achieve a significant accuracy boost (>7%) over the baseline full-precision network while reducing model size by a factor of 4x or more. Our project website is at: https://saqibjaved1.github.io/MQAT_/

翻译：在协作机器人和航天器交会对接等边缘应用中，需要在资源受限的嵌入式平台上实现高效的6D物体姿态估计。现有的6D姿态估计网络通常规模过大，难以部署于此种场景，因此必须在保持可靠性能的同时进行模型压缩。为应对这一挑战，我们提出了模块化量化感知训练（MQAT），这是一种自适应混合精度量化感知训练策略，它充分利用了现代6D姿态估计架构的模块化结构。MQAT引导一种系统化的渐进式模块量化序列，并确定各模块特定的比特精度，从而生成的量化模型性能优于当前最先进的均匀量化与混合精度量化技术所产出的模型。我们的实验展示了MQAT在不同数据集、架构及量化算法上的通用性。值得注意的是，经MQAT训练的量化模型在将模型尺寸减小4倍或更多的同时，相较于基线全精度网络实现了显著的精度提升（>7%）。我们的项目网站位于：https://saqibjaved1.github.io/MQAT_/