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

Source: [VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view](<https://www.sota2.com/research/paper/68fa65f31e209131cea0d57b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:39:29.972Z

Authors: Hager Radi, Waleed Ali

Published: 2019-06-12T16:22:41.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.11873>)

[PDF](<https://arxiv.org/pdf/1906.11873.pdf>)