Three-dimensional object recognition using an extensible local surface descriptor

We present an extensible local feature descriptor that can encode both geometric and photometric information. We first construct a unique and stable local reference frame (LRF) using the sphere neighboring points of a feature point. Then, all the neighboring points are transformed with the LRF to keep invariance to transformations. The sphere neighboring region is divided into several sphere shells. In each sphere shell, we calculate the cosine values of the point with the x-axis and z-axis. These two values are then mapped into two one-dimensional (1-D) histograms, respectively. Finally, all of the 1-D histograms are concatenated to form the signature of position angles histogram (SPAH) feature. The SPAH feature can easily be extended to a color SPAH (CSPAH) by adding another 1-D histogram generated by the photometric information of each point in each shell. The SPAH and CSPAH were rigorously tested on several common datasets. The experimental results show that both feature descriptors were highly descriptive and robust under Gaussian noise and varying mesh decimations. Moreover, we tested our SPAH-and CSPAH-based three-dimensional object recognition algorithms on four standard datasets. The experimental results show that our algorithms outperformed the state-of-the-art algorithms on these datasets. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)