The field of keypoint extraction, which is essential for vision applications
like Structure from Motion (SfM) and Simultaneous Localization and Mapping
(SLAM), has evolved from relying on handcrafted methods to leveraging deep
learning techniques. While deep learning approaches have significantly improved
performance, they often incur substantial computational costs, limiting their
deployment in real-time edge applications. Efforts to create lightweight neural
networks have seen some success, yet they often result in trade-offs between
efficiency and accuracy. Additionally, the high-dimensional descriptors
generated by these networks poses challenges for distributed applications
requiring efficient communication and coordination, highlighting the need for
compact yet competitively accurate descriptors. In this paper, we present
EdgePoint2, a series of lightweight keypoint detection and description neural
networks specifically tailored for edge computing applications on embedded
system. The network architecture is optimized for efficiency without
sacrificing accuracy. To train compact descriptors, we introduce a combination
of Orthogonal Procrustes loss and similarity loss, which can serve as a general
approach for hypersphere embedding distillation tasks. Additionally, we offer
14 sub-models to satisfy diverse application requirements. Our experiments
demonstrate that EdgePoint2 consistently achieves state-of-the-art (SOTA)
accuracy and efficiency across various challenging scenarios while employing
lower-dimensional descriptors (32/48/64). Beyond its accuracy, EdgePoint2
offers significant advantages in flexibility, robustness, and versatility.
Consequently, EdgePoint2 emerges as a highly competitive option for visual
tasks, especially in contexts demanding adaptability to diverse computational
and communication constraints.

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