FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data

Georg Krispel, Michael Opitz, Georg Waltner, Horst Possegger, Horst Bischof; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1874-1883

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 KITTI LiDAR data recording speed.

Related Material


[pdf]
[bibtex]
@InProceedings{Krispel_2020_WACV,
author = {Krispel, Georg and Opitz, Michael and Waltner, Georg and Possegger, Horst and Bischof, Horst},
title = {FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}