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Xiang Xu

at leapmotor

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Proceedings Article | 12 May 2022 Paper

Semantic-aware object detection for 3D point cloud

Xiang Xu, Gang Huang, Laifeng Hu, Yaonong Wang

Proceedings Volume 12173, 1217318 (2022) https://doi.org/10.1117/12.2634724

KEYWORDS: Clouds, Convolution, Head, Modulation, Computer programming, 3D modeling, Sensors, Image segmentation, 3D image processing, Performance modeling

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In this paper, we propose a semantic-aware network (SA-Net) to improve the performance of 3D point cloud object detection, which embeds a backward attention module and a semantic attention module. The backward attention module utilizes high-level semantic features from the encoder via fusing multi-level encoder features hierarchically. In this stage, high-level features are transformed into an attention map to modulate low-level features backward. Meanwhile, semantic attention module obtains a semantic segmentation map of a given point cloud scene through supervised learning. This can be transformed into a semantic attention map and embedded into the detection head for better detection. Equipped with these modules, SA-Net can greatly improve the performance of object detection. Extensive experiments on KITTI demonstrate that the proposed method can achieve competitive results against the state-of-the-art methods.

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