VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view

2019-06-12 · Hager Radi, Waleed Ali

This paper introduces VolMap, a real-time approach for the semantic segmentation of a 3D LiDAR surrounding view system in autonomous vehicles. We designed an optimized deep convolution neural network that can accurately segment the point cloud produced by a 360\degree{} LiDAR setup, where the input consists of a volumetric bird-eye view with LiDAR height layers used as input channels. We further investigated the usage of multi-LiDAR setup and its effect on the performance of the semantic segmentation task. Our evaluations are carried out on a large scale 3D object detection benchmark containing a LiDAR cocoon setup, along with KITTI dataset, where the per-point segmentation labels are derived from 3D bounding boxes. We show that VolMap achieved an excellent balance between high accuracy and real-time running on CPU.

📄 PDF Abstract BibTeX arXiv:1906.11873

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Tasks

3D Object Detection Autonomous VehiclesCPUobject-detection Object Detection Segmentation Semantic Segmentation

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