Index: sparse_sample/damkjer_sparse_sample.tex
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--- sparse_sample/damkjer_sparse_sample.tex	(revision 5)
+++ sparse_sample/damkjer_sparse_sample.tex	(revision 6)
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 %TODO Provide overview of paper structure.
 \section{Prior Work}
-Point cloud thinning and model simplification are not new areas of research in computer graphics and related fields relying on point-based model representations. Mesh-based approaches for model simplification have long been considered in 3D graphics to support efficient rendering and representation of complex models in real-time applications. Mesh-free approaches have been largely considered to aid in surface reconstruction from unorganized point data, usually from laser-scanned data. {\color{brickred} TODO: Add references. Beware weasel words and blind assertions.}
+Point cloud thinning and model simplification strategies . Mesh-based approaches for model simplification have long been considered in 3D graphics to support efficient rendering and representation of complex models in real-time applications. Mesh-free approaches have been largely considered to aid in surface reconstruction from unorganized point data, usually from laser-scanned data. {\color{brickred} TODO: Add references. Beware weasel words and blind assertions.}
 
 Moenning and Dodgson present an approach to model simplification using Fast Marching farthest point sampling for implicit surfaces and point clouds. Their approach operates in a coarse-to-fine manner subject to user controlled density guarantees. They also present options uniform or feature-driven point selection. \cite{Moenning:2003}
@@ -127,5 +127,5 @@
 Dyn et al present a related approach using recursive sub-sampling driven by local surface approximation. Their approach operates in a fine-to-coarse manner driven by a desired terminal point set size. Their point selection metric is solely based on a significance criterion, and the input point cloud geometry. \cite{Dyn:2008}
  
-Similar to Dyn, Yu et al present an approach that enforces a post-condition of a terminal point set size. Their approach differs from those previously discussed by operating in an adaptive manner driven by point clustering and user-specified simplification criteria and optimization process. \cite{Yu:2010}
+Yu et al also present an approach that enforces a post-condition of a terminal point set size. Their approach differs from those previously discussed by operating in an adaptive manner driven by point clustering and user-specified simplification criteria and optimization process. \cite{Yu:2010}
 
 While all of these approaches operate without generating an explicit mesh surface, they implicitly carry forward the legacy of mesh-based approaches by limiting their analysis to spatial coordinates. They also largely operate under the assumption that they are selecting points that appear significant for participation in a local surface. When considering remotely sensed LiDAR data, several of these assumptions are invalidated. Scenes imaged by LiDAR sensors are complex and contain significant points belonging to linear, planar, and isotropic structures. LiDAR data also frequently contains additional intensity or color data. These additional dimensions may contain content that is salient to end-user applications, but is not discoverable through analysis of the spatial dimensions alone. Points may also be attributed with any number of features that should be preserved through the simplification process, suggesting the need for a multi-dimensional approach.