Compression of LIDAR point cloud offers many challenges to the signal processing community. Compression schemes
must preserve both the numerical and geometrical aspects of the data, while dealing with the sparsely distributed threedimensional
nature of it. Very few effective compression methods have been developed for this type of data, and only a
handful of those methods offer the advantages of scalability. The focus of this research and development activity was to
design and implement a series of preprocessing techniques that address the common obstacles found when pursuing
scalable LIDAR point cloud compression. Three main areas being addressed are spatial scalability by means of effective
indexing techniques; range reduction and redundancy exploitation; and resolution scalability by means of sub-band
decomposition and sampling. These techniques will be combined with two different entropy encoding schemes –namely
LZW and MQ encoding, yielding scalable 12:1 compression rates.
The three-dimensional (3-D) nature and the unorganized structure of topographic LIDAR data pose several challenges
for target recognition tasks. In the past, several approaches have applied two-dimensional transformations such as spinimages
or Digital Elevation Maps (DEMs) as an intermediate step for analyzing the 3-D data with two-dimensional
(2-D) methods. However, these techniques are computationally intensive and often sacrifice some of the overall
geometrical relationship of the target points.
In this paper, we present a simple and efficient 3-D spatial transformation that preserves the geometrical attributes of the
LIDAR data in all its dimensions. This transformation permits the utilization of well established statistical and shapebased
descriptors for the implementation of an automatic target recognition algorithm. We evaluate our transformation
and analysis technique on a set of simulated LIDAR point clouds of ground vehicles with varied obstructions and noise
levels. Classification results demonstrate that our approach is efficient, tolerant to scale, rotation, and robust to noise and
other degradations.
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