Graph Convolutional Network for Point Cloud Panoptic Segmentation
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Solution Overview
Problem
Existing panoptic segmentation methods for point clouds are computationally expensive due to their reliance on complex deep learning models, making real-time implementation challenging.
Innovation Solution
The proposed method employs a graph-based approach using graph convolutional neural networks (GCNNs) for node and edge classification, leveraging semantic labels to predict instance labels without relying on complex deep learning models, thereby reducing computational requirements. This involves constructing a graph from unified cluster feature vectors and using adjacency matrices to determine edge relationships, allowing for efficient panoptic segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex deep learning models are used for panoptic segmentation, then segmentation accuracy is improved, but computational cost increases
Solution Approach 1:
The patent segments the panoptic segmentation task into two distinct stages: (1) semantic segmentation to identify object categories, and (2) instance segmentation to differentiate individual objects within those categories. This division allows each stage to use appropriately complex models, reducing overall computational cost while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary clustering step that groups points into superpoints before instance segmentation. These superpoints serve as intermediate representations that simplify the subsequent graph-based instance segmentation, reducing the computational burden on the deep learning model while preserving segmentation accuracy.
2Measurement precision
If complex deep learning models are used for panoptic segmentation, then segmentation accuracy is improved, but processing speed decreases
Solution Approach 1:
By dividing the task into semantic and instance segmentation stages, the patent enables parallel processing of different point cloud regions and reduces the depth of the neural network required at each stage, thereby improving processing speed while maintaining overall accuracy.
Solution Approach 2:
The patent performs preliminary semantic segmentation and clustering before instance segmentation. This preliminary action reduces the complexity of the subsequent instance segmentation task, allowing for faster processing in real-time applications.
3Measurement precision
If complex deep learning models are used for panoptic segmentation, then segmentation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex panoptic segmentation into two simpler sub-tasks: semantic segmentation and instance segmentation. Each sub-task uses a less complex model than a single comprehensive model, reducing overall system complexity while maintaining accuracy.
Solution Approach 2:
The clustering-based superpoint representation serves as an intermediary that simplifies the input to the instance segmentation model, reducing its complexity. The graph convolutional network operates on these simplified superpoints rather than raw point cloud data, lowering the model's computational requirements.
Data Source
AI summary
In methods and systems for graph-based panoptic segmentation of point clouds, points of a point cloud are received with a semantic label from a first category. Further, a plurality of unified cluster feature vectors from a second category are received, each being extracted from a cluster of points in the point cloud. Nodes of a constructed graph represent the unified feature vectors, and edges indicate the relationship between pairs of nodes. The edges are represented as an adjacency matrix indicating the existence or absence of an edge between pairs of nodes. A graph convolutional neural network uses the graph to predict an instance label for each node or an attribute for each edge, wherein the attribute of each edge is used for assigning the instance label to each node.