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

arXiv: https://arxiv.org/abs/1906.11873 · HTML: https://mlsanity.com/paper/1906.11873/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Semantics-Guided Moving Object Segmentation with 3D LiDAR](https://mlsanity.com/paper/2205.03186/) (2022)
- [Lidar Annotation Is All You Need](https://mlsanity.com/paper/2311.04777/) (2023)

## Abstract (arXiv)

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 360degree 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.