BIFNOM: Binary-Coded Features on Normal Maps

Sensors (Basel). 2021 May 16;21(10):3469. doi: 10.3390/s21103469.


We propose a novel method for detecting features on normal maps and describing binary features, called BIFNOM, which is three-dimensionally rotation invariant and detects and matches interest points at high speed regardless of whether a target is textured or textureless and rigid or non-rigid. Conventional methods of detecting features on normal maps can also be applied to textureless targets, in contrast with features on luminance images; however, they cannot deal with three-dimensional rotation between each pair of corresponding interest points due to the definition of orientation, or they have difficulty achieving fast detection and matching due to a heavy-weight descriptor. We addressed these issues by introducing a three dimensional local coordinate system and converting a normal vector to a binary code, and achieved more than 750fps real-time feature detection and matching. Furthermore, we present an extended descriptor and criteria for real-time tracking, and evaluate the performance with both simulation and actual system.

Keywords: binary; feature point; real-time tracking; surface normal.