3D Sparse Convolutional Neural Network for Point Cloud Segmentation
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Solution Overview
Problem
Current machine learning techniques are not effectively extendible to process 3D or higher dimensional data, such as point clouds, for tasks like semantic segmentation, due to challenges in handling unstructured and sparse data, leading to inefficiencies and loss of fine details in perception tasks for autonomous driving.
Innovation Solution
The implementation of sparse intra-channel attention, inter-channel attention, and residual tower modules within a 3D sparse convolutional neural network (CNN) model enables more efficient processing and richer feature extraction for 3D point clouds, improving semantic segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional machine learning techniques are used to process 3D point cloud data, then the system can handle unstructured higher dimensional data, but the processing efficiency decreases and fine details are lost
Solution Approach 1:
The patent divides the 3D point cloud processing into multiple hierarchical levels (coarse to fine segmentation). The system processes point clouds at different resolutions and scales, segmenting the complex processing task into manageable stages that preserve fine details while maintaining processing efficiency.
Solution Approach 2:
The patent introduces a temporal dimension by processing multiple frames of point cloud data sequentially. This allows the system to leverage temporal information to enhance spatial understanding, improving both processing efficiency and detail preservation through frame-based accumulation and refinement.
2Measurement precision
If conventional machine learning techniques are used for semantic segmentation, then the system can process unstructured data, but the accuracy of semantic segmentation decreases
Solution Approach 1:
The patent segments the semantic segmentation task into multiple hierarchical levels, processing objects at different scales and resolutions separately. This allows the system to achieve high accuracy for both large and small objects without requiring an overly complex model, as each segmentation level can use appropriately scaled processing.
Solution Approach 2:
The patent performs preliminary processing of point cloud data by organizing it into a structured format before feeding it to the machine learning model. This pre-processing step includes hierarchical organization and temporal framing, which simplifies the subsequent processing tasks and improves segmentation accuracy without increasing model complexity.
3Adaptability or versatility
If the system processes higher dimensional unstructured data, then the system can handle 3D point clouds, but the computational resources required increase
Solution Approach 1:
The patent segments the computational processing into hierarchical levels, processing point clouds at different resolutions and scales separately. This allows the system to use computational resources more efficiently by applying complex processing only where necessary (at higher levels for detailed analysis) while using simpler processing at lower levels for general classification.
Solution Approach 2:
The patent performs preliminary organization of point cloud data into a structured hierarchical format before processing. This pre-organization step reduces the computational burden during actual processing by providing the machine learning models with pre-structured input, thereby reducing overall computational resource consumption while maintaining versatility in handling unstructured 3D data.
Data Source
AI summary
Systems and methods are disclosed for processing sparse tensors using a trained neural network model. An input sparse tensor may represent a sparse input point cloud. The input sparse tensor is processed using an encoder stage having a series of one or more encoder blocks, wherein each encoder block includes a sparse convolution layer, a sparse intra-channel attention module, a sparse inter-channel attention module, and a sparse residual tower module. Output from the encoder stage is processed using a decoder stage having a series of one or more decoder blocks, wherein each decoder block includes a sparse transpose convolution layer, a sparse inter-channel attention module, and a sparse residual tower module. The output of the decoder stage is an output sparse tensor representing a sparse labeled output point cloud.