ZippyPoint: 混合精度離散化による高速な特徴点検出・記述・マッチング
ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization
ネットワーク量子化とバイナリ記述子の正規化層を導入し、計算資源が限られたロボットやARデバイス向けに、フル精度版より10倍以上高速で軽量な特徴点検出・記述・マッチング手法を実現した。
著者: Menelaos Kanakis, Simon Maurer, Matteo Spallanzani, Ajad Chhatkuli, Luc Van Gool
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Efficient detection and description of geometric regions in images is a prerequisite in visual systems for localization and mapping. Such systems still rely on traditional hand-crafted methods for efficient generation of lightweight descriptors, a common limitation of the more powerful neural network models that come with high compute and specific hardware requirements. In this paper, we focus on the adaptations required by detection and description neural networks to enable their use in computationally limited platforms such as robots, mobile, and augmented reality devices. To that end, we investigate and adapt network quantization techniques to accelerate inference and enable its use on compute limited platforms. In addition, we revisit common practices in descriptor quantization and propose the use of a binary descriptor normalization layer, enabling the generation of distinctive binary descriptors with a constant number of ones. ZippyPoint, our efficient quantized network with binary descriptors, improves the network runtime speed, the descriptor matching speed, and the 3D model size, by at least an order of magnitude when compared to full-precision counterparts. These improvements come at a minor performance degradation as evaluated on the tasks of homography estimation, visual localization, and map-free visual relocalization. Code and models are available at https://github.com/menelaoskanakis/ZippyPoint.
