Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation

Matthew N. Henry

3D pedestrian detection is just one of the most tough tasks in autonomous driving. It normally uses two knowledge acquisition resources: LiDAR and digital camera. Combining these two sensors should be helpful even so, poor fusion solutions lead to even worse benefits than LiDAR-only detection.

A current paper on proposes a approach that is able to use fusion at various degrees. It encodes semantic functions attained from the graphic in voxels and fuses them with geometric functions from the LiDAR position cloud. The fusion can be carried out at various degrees for case in point, early fusion combines data in the enter phase, and in the late fusion particular functions from both equally inputs are figured out and fused before the prediction. The recommended approach outperformed existing detection solutions through experiments. Additionally, it was productive in tough cases, for occasion, when a pedestrian walks out quickly in front of parked cars and trucks.

3D pedestrian detection is a tough job in automated driving simply because pedestrians are fairly modest, usually occluded and conveniently puzzled with narrow vertical objects. LiDAR and digital camera are two commonly made use of sensor modalities for this job, which should deliver complementary data. Unexpectedly, LiDAR-only detection solutions tend to outperform multisensor fusion solutions in public benchmarks. Lately, PointPainting has been introduced to eliminate this functionality drop by successfully fusing the output of a semantic segmentation community alternatively of the uncooked graphic data. In this paper, we propose a generalization of PointPainting to be able to use fusion at various degrees. After the semantic augmentation of the position cloud, we encode uncooked position knowledge in pillars to get geometric functions and semantic position knowledge in voxels to get semantic functions and fuse them in an productive way. Experimental benefits on the KITTI examination established demonstrate that SemanticVoxels achieves state-of-the-artwork functionality in both equally 3D and bird’s eye check out pedestrian detection benchmarks. In unique, our approach demonstrates its energy in detecting tough pedestrian cases and outperforms existing state-of-the-artwork ways.

Url: muscles/2009.12276

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