3D Sonar Volume Rendering via Spatial Binning
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Solution Overview
Problem
Sonar data is typically very sparse and noisy, making it difficult to produce high-quality images, as existing methods struggle to effectively interpret and render three-dimensional sonar data from sparse and ensonified volumes, particularly in comparison to denser data from other imaging technologies like medical imaging or oil exploration.
Innovation Solution
The method involves receiving sonar range and intensity data from a two-dimensional sonar receiver, adding intensity and slope data to fixed three-dimensional bins in World Space, and using bin opacity and normal vector calculations to enhance image quality by distributing ping point values among bins and calculating bin normals from vector sums, thereby improving image rendering and reducing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sonar data processing methods are used, then the processing complexity is low, but the image quality is poor due to sparse and noisy data
Solution Approach 1:
The patent segments the 3D sonar data into discrete volumetric bins, organizing sparse ping data into a structured grid system. This segmentation allows for systematic processing of noisy data by assigning each data point to specific spatial bins, improving image quality through organized accumulation of intensity and slope information across multiple pings.
Solution Approach 2:
The patent transforms 2D sonar receiver data into 3D volumetric representation by adding a temporal dimension (multiple pings) and organizing data in three-dimensional bins. This dimensional transformation allows sparse 2D measurements to be reconstructed into coherent 3D images, significantly improving image quality from the original sparse data.
2Reliability
If data from multiple pings are accumulated to improve image quality, then the noise is reduced, but the processing time increases
Solution Approach 1:
The patent performs preliminary organization of sonar data into volumetric bins during the data acquisition phase, assigning intensity and slope values to appropriate bins as each ping is received. This preliminary action structures the data before full processing, enabling more efficient accumulation across multiple pings and reducing the computational burden during image reconstruction.
Solution Approach 2:
The patent creates multiple copies of the bin structure for different spatial volumes and accumulates data across multiple pings by adding to these pre-created bin copies. This copying approach allows parallel accumulation of data from multiple pings without sequential processing, reducing overall processing time while maintaining improved image quality through noise reduction.
Data Source
AI summary
Sonar imaging data obtained by sending multiple sonar pings towards an object is reduced by assigning values measured by each sonar ping to bins, where each bin is fixed in World Space, and calculating the opacity of each bin to produce an image of the object. A normal vector associated with each bin is used to calculate the light reflected from a model constructed from the data. The most preferred normal vector is calculated from the vector sum of normals calculated from each ping.