# A Local Discrete Feature Histogram for Point Cloud Feature Representation
## 2 Literature Review  
In LRF-based descriptors, the signature of histograms of orientations (SHOT) descriptor was introduced by [PERSON] et al. [13], which separates the local space into multiple subspaces along the radial, azimuthal, and elevation directions in polar coordinates and performs deviation angle statistics within each subspace. The rotation projection statistics (RoPS) descriptor was introduced by [PERSON] et al. [14], which continuously rotates a local surface within the LRF and projects the rotated surface to compute the statistical quantities of the projected point density, finally concatenating these statistics to obtain the rotation projection statistical feature. A triple orthogonal local depth images (TOLDI) descriptor was introduced by [PERSON] et al. [15], which generates a feature vector by concatenating three local depth images taken from three orthogonal view planes within the LRF. Concatenating the depth and contour data obtained from three orthogonal view planes within the LRF into a single vector is how [PERSON] et al. [16] generated the multi-view depth and contour signatures (MDCS) descriptor. A trigonometric projection statistics histograms (TPSH) descriptor was introduced by [PERSON] et al. [17], which constructs the LRF using a multi-attribute weighting strategy and generates geometric and spatial distribution images using a triangular projection mechanism, encoded as statistical histograms. The histograms of the dual deviation angle feature (HDDAF) descriptor were introduced by [PERSON] et al. [18], who construct two deviation angle feature histograms by calculating the centroid connection line between the local spherical neighborhood and the LRF origin, as well as angles between this line and the LRF, and between this and the normal vector.  
In LRA-based descriptors, a local feature statistics histogram (LFSH) descriptor was proposed by [PERSON] et al. [4], which generates the LFSH descriptor by encoding the statistical characteristics of the local depth, horizontal projection distance, and normal deviation angle. The statistic of deviation angles on subdivided space (SDASS) descriptor was introduced by [PERSON] et al. [19], which proposes the local minimum axis (LMA) instead of the normal to reduce the impact of interference, uses height and projected radial directions to divide local space, and calculates the deviation angle between the LMA and LRA in each subspace. A divisional local feature statistics (DLFS) descriptor was introduced by [PERSON] and [PERSON] [20],which partitions the local space along the projected radial direction and computes statistical operations on three geometric attributes and one spatial attribute within each partition.  
In descriptors without any local reference system, the point feature histograms (PFH) descriptor was proposed by [PERSON] et al. [21], which encodes local surface shape information and is highly discriminative but computationally costly. To address this, [PERSON] et al. [22] used a simplified version of the PFH to generate the fast point feature histogram (FPFH). The descriptor named histograms of point pair features (HoPPF) was developed by [PERSON] et al. [23], which generates corresponding sub-features from the distribution of local point pairs in each of the eight regions that make up the local point pair set of each key point. These sub-features are then concatenated into a vector. The point pair transformation feature histograms (PPTFH) descriptor was introduced by [PERSON] et al. [24], which divides the point pair set into four subsets, creates three feature histograms for each subset using point pair transformation features, and concatenates the histograms from all subsets into a feature vector.