Title

Mesh-Free Sparse Representation Of Multidimensional Lidar Data

Keywords

LiDAR; mesh-free simplification; multidimensional systems; point cloud; principal component analysis

Abstract

Modern LiDAR collection systems generate very large data sets approaching several million to billions of point samples per product. Compression techniques have been developed to help manage the large data sets. However, sparsifying LiDAR survey data by means other than random decimation remains largely unexplored. In contrast, surface model simplification algorithms are well-established, especially with respect to the complementary problem of surface reconstruction. Unfortunately, surface model simplification algorithms are often not directly applicable to LiDAR survey data due to the true 3D nature of the data sets. Further, LiDAR data is often attributed with additional user data that should be considered as potentially salient information. This paper makes the following main contributions in this area: (i) We generalize some features defined on spatial coordinates to arbitrary dimensions and extend these features to provide local multidimensional statistics. (ii) We propose an approach for sparsifying point clouds similar to mesh-free surface simplification that preserves saliency with respect to the multidimensional information content. (iii) We show direct application to LiDAR data and evaluate the benefits in terms of level of sparsity versus entropy.

Publication Date

1-28-2014

Publication Title

2014 IEEE International Conference on Image Processing, ICIP 2014

Number of Pages

4682-4686

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICIP.2014.7025949

Socpus ID

84949927954 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84949927954

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