Incremental Sparse 3D Convolution for Real-Time Point Cloud Processing
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Solution Overview
Problem
Existing incremental convolution methods are unsuitable for sparse convolutions used in 3D segmentation and detection tasks, particularly in applications like autonomous driving and robotics, due to their inefficiency and inability to handle the additional dimension of 3D point cloud data effectively.
Innovation Solution
A method utilizing incremental sparse 3D convolution, specifically incorporating incremental submanifold sparse convolution (INS-SSC) layers and incremental non-linear layers, to process 3D point cloud data, enabling efficient computation by constraining active residual sites and propagating residuals, thereby accelerating network prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single-frame computation method is used for 3D point cloud data, then each frame can be processed independently, but redundant computations occur due to duplicate information between frames resulting in slow processing speed
Solution Approach 1:
The patent performs preliminary computation on the first frame to generate feature maps, which are then cached and reused for subsequent frames. This preliminary action eliminates the need to recompute identical features for duplicate information in consecutive frames, significantly reducing redundant computations and improving processing speed for video tasks.
2Productivity
If incremental convolution methods are used, then computation efficiency is improved, but they are unsuitable for sparse convolutions used in 3D segmentation and detection tasks
Solution Approach 1:
The patent applies incremental computation selectively to specific layers of the neural network architecture. The first layer performs incremental computation on input data to generate intermediate feature maps, while subsequent layers perform incremental computation on these feature maps. This localized application of incremental computation to different stages of the network enables both efficiency improvement and adaptability to sparse 3D convolution tasks.
Solution Approach 2:
The patent divides the neural network into multiple layers, with each layer performing incremental computation independently. The first layer processes input data to generate first intermediate data, and the second layer processes this intermediate data to generate output data. This segmentation allows incremental computation to be applied at multiple levels, making it suitable for complex sparse 3D convolution tasks while maintaining computation efficiency.
3Measurement precision
If 3D point cloud data is processed using traditional methods, then comprehensive analysis can be achieved, but computational complexity increases making real-time processing difficult
Solution Approach 1:
The patent maintains continuous processing of 3D point cloud data through incremental computation. Instead of processing each frame independently from scratch, the system continuously updates feature maps by computing only the differences between consecutive frames. This continuous action preserves comprehensive analysis capability while reducing computational complexity, enabling real-time processing of 3D data for applications like autonomous driving and robotics.
Data Source
AI summary
A method for processing three-dimensional (3D) point cloud data based on incremental sparse 3D convolution is provided. A computer device obtains 3D point cloud data and forms a training set by processing the 3D point cloud data. The computer device constructs and trains a sparse 3D convolutional network model by inputting the training set. The computer device constructs an incremental sparse 3D convolutional network model by performing incremental replacement of sparse convolutional layers of the trained sparse 3D convolutional network model. The computer device inputs real-time 3D point cloud data into the incremental sparse 3D convolutional network model, and determines an output result as a result of processing the real-time 3D point cloud data. Processing of the 3D point cloud data at least includes 3D semantic segmentation, target detection, 3D classification and video processing.