# An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation

arXiv: https://arxiv.org/abs/2108.07661 · HTML: https://mlsanity.com/paper/2108.07661/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)
- [SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud](https://mlsanity.com/paper/1710.07368/) (2017)
- [SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud](https://mlsanity.com/paper/1809.08495/) (2018)
- [SEGCloud: Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1710.07563/) (2017)
- [Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark](https://mlsanity.com/paper/1704.03847/) (2017)
- [PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/1807.00652/) (2018)
- [RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/1906.00208/) (2019)
- [Classification of Point Cloud Scenes with Multiscale Voxel Deep Network](https://mlsanity.com/paper/1804.03583/) (2018)

## Abstract (arXiv)

LiDARs and cameras are the two main sensors that are planned to be included in many announced autonomous vehicles prototypes. Each of the two provides a unique form of data from a different perspective to the surrounding environment. In this paper, we explore and attempt to answer the question: is there an added benefit by fusing those two forms of data for the purpose of semantic segmentation within the context of autonomous driving? We also attempt to show at which level does said fusion prove to be the most useful. We evaluated our algorithms on the publicly available SemanticKITTI dataset. All fusion models show improvements over the base model, with the mid-level fusion showing the highest improvement of 2.7% in terms of mean Intersection over Union (mIoU) metric.