Large-scale spatial data visualization method based on augmented reality

Background

A task assigned to space exploration satellites involves detecting the physical environment within a certain space. However, space detection data are complex and abstract. These data are not conducive for researchers' visual perceptions of the evolution and interaction of events in the space environment.

Methods

A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time, and the corresponding relationships between data location features and other attribute features were established. A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data. The visualization process is optimized for rendering by merging materials, reducing the number of patches, and performing other operations.

Results

The results of sampling, feature extraction, and uniform visualization of the detection data of complex types, long duration spans, and uneven spatial distributions were obtained. The real-time visualization of large-scale spatial structures using augmented reality devices, particularly low-performance devices, was also investigated.

Conclusions

The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space, express the structure and changes in the spatial environment using augmented reality, and assist in intuitively discovering spatial environmental events and evolutionary rules.