Virtual reality improves understanding of dense forest 3D data
Immersive Focus+Context Techniques to Assist in Interpretation of High Density Forest Point Clouds
Human-Computer Interaction
Summary
Forests can be mapped using lots of 3D points to show every tree and leaf, but these clouds of points are very hard to understand on regular screens. The authors created three new virtual reality tools that let users look closely at detailed parts of the forest data while still seeing the overall picture. They tested these tools and found that virtual reality helped people analyze forest data more quickly and accurately compared to normal computers. Their new techniques could make forest management easier by helping experts explore complex 3D data in more natural and effective ways.
What this means in practice
- •For forestry management teams: Perform faster and more accurate forest inventory assessments using immersive VR visualization of dense 3D point cloud data.
- •For urban planners: Use immersive focus+context VR tools to better analyze dense 3D environmental data for city green space planning and maintenance.
Authors
Spencer O'Keeffe, Jan Rombouts, Bruce H Thomas, Michelle Balasso, Jim O'Hehir, Andrew Cunningham
Abstract
Virtual Reality (VR) is changing how we interact with complex spatial datasets. VR has the potential within forestry to help analyse remotely sensed spatial data for inventory management. However, challenges of scale and legibility of dense forest point clouds must be addressed. To do so, we present three VR Focus+Context (F+C) techniques: Wedgelight, Remote Lantern, and Ray Lantern. These techniques present point cloud data using a constrained highdetail focus within a broader abstracted context representation, manipulated using egocentric and exocentric interactions. We conducted two user studies. The first informed the design of the F+C techniques. The results from the first study indicate that VR significantly outperforms desktop displays in basic analysis of forestry point clouds, with improvements to both time and accuracy. The second study evaluated the performance of the resulting F+C techniques in a task analogous to real-world forestry operations. Our F+C techniques significantly outperformed current approaches, confirming their viability for facilitating detailed and effective forest assessments. These findings indicate that VR has significant advantages for spatial data analysis tasks in forestry, making a strong argument for its adoption in future analytical frameworks. The F+C visualisation techniques we introduced show considerable promise for analysis of high density point clouds, opening new avenues for development in immersive analytics. Supplemental materials (code, scans, results) are available at https://osf.io/hqc9u.