SqueezeSeg
A convolutional neural network model for real-time road-object segmentation from 3D LiDAR point clouds.
What is SqueezeSeg?
SqueezeSeg is a convolutional neural network model for segmenting road objects from 3D LiDAR point clouds in real-time. It projects LiDAR data onto a 2D spherical surface and uses a CNN architecture combined with recurrent CRFs to identify objects like cars and cyclists. The model addresses the challenge of processing sparse, unstructured point cloud data for autonomous driving perception systems.
Researchers and engineers working on autonomous driving systems, particularly those focused on LiDAR-based perception and real-time object detection. It's also relevant for computer vision practitioners interested in point cloud segmentation techniques.
Developers choose SqueezeSeg because it provides an efficient, real-time solution specifically designed for LiDAR segmentation with a publicly available TensorFlow implementation. Its integration of recurrent CRFs with CNNs offers improved accuracy while maintaining performance suitable for autonomous driving applications.
Overview
Implementation of SqueezeSeg, convolutional neural networks for LiDAR point clout segmentation
Use Cases
Best For
- Real-time object segmentation from LiDAR point clouds in autonomous vehicles
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