3D Dynamic Sparse Convolution for Efficient 3D Visual Recognition
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Solution Overview
Problem
3D Convolutional Neural Networks (CNNs) face significant computational and memory challenges due to their cubic growth in computation and memory requirements, which is exacerbated by the additional spatial dimension in 3D data compared to 2D data.
Innovation Solution
The introduction of 3D Dynamic Sparse Convolution (3DSC), which involves input feature map partitioning, shared 3D dynamic sparse convolution, and output feature map grouping, along with a dynamic mechanism for generating shared convolution kernels and a multi-dimensional attention mechanism for modulating these kernels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 3D CNNs are used for 3D visual recognition tasks, then recognition performance is improved, but computational requirements and memory usage increase cubically
Solution Approach 1:
The patent divides the input feature map into multiple disjoint groups and applies separate convolutional kernels to each group. This segmentation approach reduces the computational complexity from cubic growth to linear growth by processing only relevant regions independently, thus resolving the contradiction between maintaining recognition performance and reducing computational requirements.
Solution Approach 2:
The patent introduces different convolutional kernels for different groups of feature maps, allowing each group to be processed with optimized local parameters. This local quality approach enables the model to adapt to specific spatial regions' characteristics, improving recognition accuracy while reducing overall computational burden compared to uniform 3D CNN processing.
2Adaptability or versatility
If 3D CNNs process data with additional spatial dimension, then 3D visual recognition capability is enhanced, but memory requirements increase cubically
Solution Approach 1:
By segmenting the input feature map into disjoint groups and applying selective convolution only to active regions, the patent reduces memory requirements from cubic growth to linear growth. This allows the system to maintain enhanced 3D visual recognition capability while significantly reducing the quantity of data that needs to be stored and processed in memory.
Solution Approach 2:
The patent extracts and processes only the relevant spatial dimensions and regions that contribute to 3D recognition tasks, rather than processing the entire 3D volume. This extraction approach maintains the essential 3D recognition capability while removing unnecessary computational and memory overhead from processing the complete cubic data space.
3Productivity
If conventional sparse 3D convolution is used, then some computational reduction is achieved, but performance is insufficient compared to dense 3D CNNs
Solution Approach 1:
The patent dynamically selects which convolutional kernels to apply based on the content and characteristics of each feature map group. This dynamic approach allows the system to adaptively balance between computational efficiency and recognition performance by applying sparse convolution only where necessary, rather than using a fixed sparse or dense convolution strategy throughout.
Solution Approach 2:
The patent changes the parameters of the convolutional operation by introducing group-specific kernels and adjusting the sparsity level dynamically. This parameter change enables the system to optimize the balance between computational efficiency and recognition performance for each specific task and data distribution, resolving the contradiction between productivity and reliability.
Data Source
AI summary
The disclosure provides an apparatus, method, device and medium for 3D dynamic sparse convolution. The method includes: receiving an input feature map of a 3D data sample; performing input feature map partition to divide the input feature map into a plurality of disjoint input feature map groups; performing a shared 3D dynamic sparse convolution to the plurality of disjoint input feature map groups respectively to obtain a plurality of output feature maps corresponding to the plurality of disjoint input feature map groups, wherein the shared 3D dynamic sparse convolution comprises a shared 3D dynamic sparse convolutional kernel; and performing output feature map grouping to sequentially stack the plurality of output feature maps to obtain an output feature map corresponding to the input feature map. (FIG. 2).