Airborne Light Detection And Ranging (LiDAR) is an increasingly important modality for remote sensing of forests. Unfortunately, the lack of smooth surfaces complicates visualization of LiDAR data and of the results of fundamental analysis tasks that interest environmental scientists. In this paper, we use multi-pass point-cloud rendering to produce shadows, approximate occlusion, and a non-photorealistic silhouette effect which enhances the perception of the three-dimensional structure. We employ these techniques to provide visualizations for evaluating two analysis techniques, tree segmentation and forest structure clustering.

We describe the implementation of a simple method for finding k approximate nearest neighbors (ANNs) on the GPU. While the performance of most ANN algorithms depends heavily on the distributions of the data and query points, our approach has a very regular data access pattern. It performs as well as state of the art methods on easy distributions with small values of k, and much more quickly on more difficult problem instances. Irrespective of the distribution and also roughly of the size of the set of input data points, we can find 50 ANNs for 1M queries at a rate of about 1200 queries/ms.