B-HoD: A lightweight and fast binary descriptor for 3D object recognition and registration

Abstract

3D object recognition and registration in computer vision applications has lately drawn much attention as it is capable of superior performance compared to its 2D counterpart. Although a number of high performing solutions do exist, it is still challenging to further reduce processing time and memory requirements to meet the needs of time critical applications. In this paper we propose an extension of the 3D descriptor Histogram of Distances (HoD) into the binary domain named the Binary-HoD (B-HoD). Our binary quantization procedure along with the proposed preprocessing step reduce an order of magnitude both processing time and memory requirements compared to current state of the art 3D descriptors. Evaluation on two popular low quality datasets shows its promising performance.

Performance evaluation of single and cross‐dimensional feature detection and description

“…Then adjacent Gaussian functions are subtracted to produce the DoG functions and this procedure repeats with down‐sampled Gaussian functions in the next octave. Lin et al [50] suggest a binary variant of the SHOT descriptor by utilising a Gray‐code encrypting scheme, while [51] proposes a binary variant of the HoD descriptor. In [52], the authors propose a deep‐learning based solution that directly processes unstructured 3D point clouds and learns a permutation invariant representation of the 3D vertices, while in [53] the authors utilise a deep network to directly match 2D with 3D features.…”