Sparse CNN Processing for Relevant Regions in LIDAR Imagery
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Solution Overview
Problem
Existing convolutional neural networks (CNNs) face significant computational and time expenses when applied to sparse imagery, such as LIDAR data, as they perform convolutions over entire images, including sparse regions that are not relevant for predictions.
Innovation Solution
The proposed solution involves a computing system that extracts relevant portions of sparse imagery, which are less than the entirety of the imagery, and applies a machine-learned convolutional neural network to these portions. This system uses sparse convolutional blocks with gather and scatter layers to perform convolutions only on non-sparse regions, thereby reducing unnecessary computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks perform convolutions over entire images including sparse regions, then complete image coverage is achieved, but computational expense and processing time increase significantly
Solution Approach 1:
The patent segments the input image into sparse and non-sparse regions, applying different processing strategies to each. Convolution operations are performed only on non-sparse regions while sparse regions are handled through interpolation or skipping, thereby reducing computational time while preserving prediction accuracy in critical areas.
Solution Approach 2:
The patent applies local quality by using different processing methods for different regions of the image. Non-sparse regions receive full convolutional processing to maintain high prediction accuracy, while sparse regions use simplified processing, optimizing the trade-off between accuracy and processing time for each local area.
2Reliability
If convolutional neural networks perform convolutions over entire images, then comprehensive feature extraction is achieved, but computational resources are wasted on sparse regions
Solution Approach 1:
The patent segments the image processing task by identifying sparse versus non-sparse regions and applying appropriate processing to each. This segmentation ensures that computational energy is concentrated on regions that contribute most to prediction reliability while minimizing energy expenditure on sparse regions with limited information content.
Solution Approach 2:
The patent applies partial action by performing complete convolution operations only where necessary (non-sparse regions) and using simplified or interpolated processing for sparse regions. This selective approach maintains prediction reliability in critical areas while reducing overall computational energy consumption.
3Adaptability or versatility
If standard convolutional operations are applied to sparse imagery, then uniform processing is maintained, but processing efficiency decreases
Solution Approach 1:
The patent introduces dynamic processing by adapting the convolution operation based on the sparsity characteristics of each region. The system dynamically adjusts processing intensity and method according to local data density, improving processing efficiency while maintaining the versatility to handle different types of sparse imagery through configurable processing strategies.
Data Source
AI summary
The present disclosure provides systems and methods that apply neural networks such as, for example, convolutional neural networks, to sparse imagery in an improved manner. For example, the systems and methods of the present disclosure can be included in or otherwise leveraged by an autonomous vehicle. In one example, a computing system can extract one or more relevant portions from imagery, where the relevant portions are less than an entirety of the imagery. The computing system can provide the relevant portions of the imagery to a machine-learned convolutional neural network and receive at least one prediction from the machine-learned convolutional neural network based at least in part on the one or more relevant portions of the imagery. Thus, the computing system can skip performing convolutions over regions of the imagery where the imagery is sparse and/or regions of the imagery that are not relevant to the prediction being sought.