Abstract
We introduce FPG-NAS, a FLOPs-aware Gated Differentiable Neural Architecture Search framework for efficient 6DoF object pose estimation. Estimating 3D rotation and translation from a single image has been widely investigated yet remains computationally demanding, limiting applicability in resource-constrained scenarios. FPG-NAS addresses this by proposing a specialized differentiable NAS approach for 6DoF pose estimation, featuring a task-specific search space and a differentiable gating mechanism that enables discrete multi-candidate operator selection, thus improving architectural diversity. Additionally, a FLOPs regularization term ensures a balanced trade-off between accuracy and efficiency. The framework explores a vast search space of approximately 10\textsuperscript{92} possible architectures. Experiments on the LINEMOD and SPEED+ datasets demonstrate that FPG-NAS-derived models outperform previous methods under strict FLOPs constraints. To the best of our knowledge, FPG-NAS is the first differentiable NAS framework specifically designed for 6DoF object pose estimation.
Abstract (translated)
我们介绍了FPG-NAS，这是一种基于FLOPs感知的门控可微神经架构搜索框架，旨在高效地进行六自由度（6DoF）物体姿态估计。从单张图像中估算3D旋转和位移已受到广泛研究，但仍具有较高的计算需求，在资源受限场景中的应用受到了限制。FPG-NAS通过提出一种专门针对6DoF姿态估计算法的可微神经架构搜索方法来解决这一问题，该方法包括任务特定的搜索空间以及一个可微门控机制，能够实现离散多候选操作选择，从而提高架构多样性。此外，引入了FLOPs正则化项以确保精度与效率之间的平衡。该框架探索了一个庞大的大约包含$10^{92}$种可能架构的搜索空间。在LINEMOD和SPEED+数据集上的实验表明，在严格的FLOPs约束条件下，由FPG-NAS生成的模型优于先前的方法。据我们所知，FPG-NAS是首个专门为六自由度物体姿态估计设计的可微神经架构搜索框架。
URL
https://arxiv.org/abs/2508.03618
