PillarNet: Real-Time and High-Performance Pillar-Based 3D Object Detection

Author:

Shi Guangsheng,Li Ruifeng,Ma Chao

Publisher

Springer Nature Switzerland

Reference53 articles.

1. Bewley, A., Sun, P., Mensink, T., Anguelov, D., Sminchisescu, C.: Range conditioned dilated convolutions for scale invariant 3D object detection. In: Conference on Robot Learning (CoRL) (2020)

2. Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 (2020)

3. Chen, Q., Sun, L., Cheung, E., Yuille, A.L.: Every view counts: cross-view consistency in 3D object detection with hybrid-cylindrical-spherical voxelization. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21224–21235 (2020)

4. Lecture Notes in Computer Science;Q Chen,2020

5. Chen, Q., Vora, S., Beijbom, O.: PolarStream: streaming lidar object detection and segmentation with polar pillars. arXiv preprint arXiv:2106.07545 (2021)

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