6D物体姿勢推定のためのモジュラー量子化対応トレーニング
Modular Quantization-Aware Training for 6D Object Pose Estimation
6D姿勢推定ネットワークのモジュール構造を活かし、混合精度の量子化対応トレーニングを段階的に行うことで、モデルを4倍以上小さくしつつ精度を7%以上向上させる手法を提案。
著者: Saqib Javed, Chengkun Li, Andrew Price, Yinlin Hu, Mathieu Salzmann
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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_/
