Improved Pillar with Fine-grained Feature for 3D Object Detection

• URL: http://arxiv.org/abs/2110.06049v1
• Date: Tue, 12 Oct 2021 14:53:14 GMT
• Title: Improved Pillar with Fine-grained Feature for 3D Object Detection
• Authors: Jiahui Fu, Guanghui Ren, Yunpeng Chen, Si Liu
• Abstract summary: 3D object detection with LiDAR point clouds plays an important role in autonomous driving perception module. Existing point-based methods are challenging to reach the speed requirements because of too many raw points. The 2D grid-based methods, such as PointPillar, can easily achieve a stable and efficient speed based on simple 2D convolution.
• Score: 23.348710029787068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D object detection with LiDAR point clouds plays an important role in autonomous driving perception module that requires high speed, stability and accuracy. However, the existing point-based methods are challenging to reach the speed requirements because of too many raw points, and the voxel-based methods are unable to ensure stable speed because of the 3D sparse convolution. In contrast, the 2D grid-based methods, such as PointPillar, can easily achieve a stable and efficient speed based on simple 2D convolution, but it is hard to get the competitive accuracy limited by the coarse-grained point clouds representation. So we propose an improved pillar with fine-grained feature based on PointPillar that can significantly improve detection accuracy. It consists of two modules, including height-aware sub-pillar and sparsity-based tiny-pillar, which get fine-grained representation respectively in the vertical and horizontal direction of 3D space. For height-aware sub-pillar, we introduce a height position encoding to keep height information of each sub-pillar during projecting to a 2D pseudo image. For sparsity-based tiny-pillar, we introduce sparsity-based CNN backbone stacked by dense feature and sparse attention module to extract feature with larger receptive field efficiently. Experimental results show that our proposed method significantly outperforms previous state-of-the-art 3D detection methods on the Waymo Open Dataset. The related code will be released to facilitate the academic and industrial study.

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