  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.
