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An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation

arXiv 2108.07661 ↗First posted 17 Aug 2021cs.CVcited by 0

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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.

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.

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13 of 19 references.

References · 13

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  3. 2018YOLOv3: An Incremental Improvementcited by 2,659
  4. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  5. 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 406
  6. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 385
  7. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 232
  8. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  9. 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 179
  10. 2017Semantic3D.net: A new Large-scale Point Cloud Classification Benchmarkcited by 158
  11. 2018PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentationcited by 58
  12. 2019RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Drivingcited by 39
1 more
  1. 2018Classification of Point Cloud Scenes with Multiscale Voxel Deep Networkcited by 11

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