Abstract
The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.
Highlights
A terrestrial laser scanner (TLS) can provide three-dimensional (3D) co-ordinates of sampled points at a pre-defined sampling interval
= ∅; results of the proposed on real-world point clouds
Instead of considering the topological relationship among points, spatial coordinate information was compressed directly based on the characteristics of broad-leaved tree point clouds
Summary
A terrestrial laser scanner (TLS) can provide three-dimensional (3D) co-ordinates of sampled points at a pre-defined sampling interval. They have high measurement accuracy and data acquisition efficiency, and are suitable for capturing large scenes with relatively low expenditure. TLS has been used to obtain 3D observations on tree surfaces to study morphological attributes [1,2,3,4,5]. The sampled data from TLS is very dense and with considerable redundancy. The vast volume of point clouds poses great challenges in real-time processing, storage, display, and transmission. It is necessary to compress massive point clouds while maintaining a certain accuracy
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