EdgePoint: Efficient Point Detection and Compact Description via Distillation

Efficient interest point detection and description in images play a crucial role in many tasks such as multi-robot SLAM and collaborative localization. To facilitate fast detection and generate compact descriptions on edge devices, we introduce EdgePoint, a lightweight neural network. We design a new detection loss UnfoldSoftmax to improve inference speed. Futhermore, we propose Ortho-Alignment loss combined with LocalPCA compression to learn compact 32-dimensional descriptors. To enable efficient storage or communication, we also quantize the generated descriptors into integral values. We perform EdgePoint on various datasets, and show that it surpasses SuperPoint in performance while utilizing only 1% of the parameters and achieving up to more than 10 times faster inference speed. By applying descriptor quantization, the requirements for storage and communication can be reduced by up to 97% without performance decreasing.