FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data

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Abstract

We introduce a simple yet effective fusion method of LiDAR and RGB data to segment LiDAR point clouds. Utilizing the dense native range representation of a LiDAR sensor and the setup calibration, we establish point correspondences between the two input modalities. Subsequently, we are able to warp and fuse the features from one domain into the other. Therefore, we can jointly exploit information from both data sources within one single network. To show the merit of our method, we extend SqueezeSeg, a point cloud segmentation network, with an RGB feature branch and fuse it into the original structure. Our extension called FuseSeg leads to an improvement of up to 18% IoU on the KITTI benchmark. In addition to the improved accuracy, we also achieve real-time performance at 50 fps, five times as fast as the recording speed of the KITTI LiDAR data.

Originalspracheenglisch
TitelProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
Seiten1863-1872
Seitenumfang10
ISBN (elektronisch)9781728165530
DOIs
PublikationsstatusVeröffentlicht - Mär 2020
Veranstaltung2020 IEEE/CVF Winter Conference on Applications of Computer Vision: WACV 2020 - Snowmass Village, USA / Vereinigte Staaten
Dauer: 1 Mär 20205 Mär 2020

Konferenz

Konferenz2020 IEEE/CVF Winter Conference on Applications of Computer Vision
KurztitelWACV 2020
LandUSA / Vereinigte Staaten
OrtSnowmass Village
Zeitraum1/03/205/03/20

ASJC Scopus subject areas

  • !!Computer Vision and Pattern Recognition
  • !!Computer Science Applications

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