PCSCNet: Fast 3D semantic segmentation of LiDAR point cloud for autonomous car using point convolution and sparse convolution network

Author:

Park JaehyunORCID,Kim ChansooORCID,Kim SoyeongORCID,Jo KichunORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference40 articles.

1. SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences;Behley,2019

2. 3DmFV: Three-dimensional point cloud classification in real-time using convolutional neural networks;Ben-Shabat;IEEE Robotics and Automation Letters,2018

3. Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020). nuScenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR).

4. Compositional prototype network with multi-view comparision for few-shot point cloud semantic segmentation;Chen,2020

5. (AF)2-s3Net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network;Cheng,2021

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