Point Cloud Filtering Using Pre-computed Quality Metrics

The patent pre-calculates and stores quality information for each point in the point cloud during the initial generation phase. This includes computing variance values and image count metrics for all points beforehand, so that subsequent filtering operations can quickly select points based on user-defined thresholds without re-processing the entire raw data set. This preliminary preparation eliminates the need to regenerate the entire point cloud when adjusting quality parameters.

The patent separates the point cloud data into individual point records, each containing coordinate information and pre-computed quality attributes. This segmentation allows the system to filter and select specific points based on quality criteria without processing the entire point cloud, enabling efficient adjustment of model detail by selecting only the necessary points rather than regenerating the complete data set.

The system performs all necessary quality calculations during the initial point cloud generation, storing variance and image count information for each point. This preliminary computation enables multiple filtering iterations with different quality thresholds to be performed rapidly on the already-processed data, eliminating the need to re-generate the point cloud for each iteration and thus maintaining high productivity while achieving the desired level of detail.

The patent implements an interactive filtering process where users can review the filtered point cloud representation and adjust quality thresholds based on the displayed results. The system provides feedback by showing the effect of different filtering criteria, allowing users to iteratively refine the selection of points until the desired model quality is achieved, all without regenerating the underlying point cloud data.

The patent pre-computes and stores multiple quality metrics for each point, including variance values and image count information. This preliminary preparation of quality data simplifies the subsequent filtering process, as users only need to specify threshold values without needing to understand or configure complex processing parameters. The system handles the complexity of quality assessment automatically during the initial processing phase.

The patent enables users to adjust quality thresholds by changing simple parameter values (such as minimum variance or minimum image count) rather than modifying complex processing algorithms. This parameter-based approach allows users to improve point cloud accuracy by adjusting straightforward quality criteria without increasing process complexity, as the underlying computation framework remains unchanged.