Continuous Convolution Kernels for Sparse 3D Point Cloud Prediction
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Solution Overview
Problem
Conventional convolutional neural networks are limited in their ability to process non-grid structured data, such as 3D point clouds from sensors, as they assume a grid structured input and perform discrete convolutions, which is inadequate for real-world applications like autonomous vehicle navigation that require processing sparse and unevenly distributed data.
Innovation Solution
The implementation of continuous convolutional neural networks with parametric continuous kernels that can perform convolutions over a full continuous vector space, allowing for the generation of predictions based on arbitrary data structures and enabling the processing of non-grid structured data, such as 3D point clouds, by using a kernel defined continuously over the support domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional discrete convolutions are used on grid-structured data, then computational simplicity is maintained, but the ability to process non-grid structured data (such as 3D point clouds) is lost
Solution Approach 1:
The patent transforms the discrete convolution operation into a continuous convolution operation by changing the parameter domain from discrete grid indices to continuous spatial coordinates. This allows the convolution kernel to operate over continuous 3D space rather than discrete 2D image grids, enabling processing of point cloud data while maintaining a convolution-based computational framework
Solution Approach 2:
The patent replaces the traditional discrete mechanical convolution process (sliding window over grid cells) with a continuous mathematical convolution integral. This substitution eliminates the need for grid-structured input by using continuous coordinate transformations and parametric kernel functions that can handle arbitrary data distributions
2Measurement precision
If dense predictions are generated from sparse data sets, then prediction accuracy improves, but memory and processing requirements increase
Solution Approach 1:
The patent segments the continuous convolution operation into discrete computational steps that can be efficiently executed. By dividing the continuous parameter space into manageable regions and processing them sequentially or in parallel batches, the system generates dense predictions from sparse input without requiring excessive memory resources
Solution Approach 2:
The patent introduces continuous parameter dimensions (such as spatial coordinates and kernel parameters) to transform the computation from a discrete high-memory operation to a continuous lower-memory operation. This dimensional transformation allows the system to generate dense output predictions by evaluating the continuous convolution integral at multiple points without storing all intermediate results
Data Source
AI summary
Systems and methods are provided for machine-learned models including convolutional neural networks that generate predictions using continuous convolution techniques. 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 perform, with a machine-learned convolutional neural network, one or more convolutions over input data using a continuous filter relative to a support domain associated with the input data, and receive a prediction from the machine-learned convolutional neural network. A machine-learned convolutional neural network in some examples includes at least one continuous convolution layer configured to perform convolutions over input data with a parametric continuous kernel.