PP-SSD: Point Painting Single Stage Detector for 3D Object Detection

Integrating multimodal data features as input for downstream modules would provide superior detection performance. Using an independent semantic model to paint point clouds constraints the performance of object detection. 2D image data only records the color information of foreground objects, using color information to enhance the features of all point clouds remains a challenging task. In our work, we proposed a simple and plug-and-play point painting method to tackle above issue. The color information of the point cloud in the same frustum is encoded using the range information before the point-based single-stage detector, integrating the position, intensity and color information. Experiments results on the KITTI datasets using 3DSSD network as point encoder validate the effectiveness of our point painting method. On the KITTI car 3D detection test, our PP-3DSSD achieves 82.71% AP.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1109/icus61736.2024.10840112
Citations
2
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
0.50
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article

PP-SSD: Point Painting Single Stage Detector for 3D Object Detection

Shiyou Chen, Yulin Liu, Huihui Wang, Hao Liu
2 citations
Advanced Neural Network Applications
0.50
article

PP-SSD: Point Painting Single Stage Detector for 3D Object Detection

Shiyou Chen, Yulin Liu, Huihui Wang, Hao Liu
article en
2 citations

Abstract

Integrating multimodal data features as input for downstream modules would provide superior detection performance. Using an independent semantic model to paint point clouds constraints the performance of object detection. 2D image data only records the color information of foreground objects, using color information to enhance the features of all point clouds remains a challenging task. In our work, we proposed a simple and plug-and-play point painting method to tackle above issue. The color information of the point cloud in the same frustum is encoded using the range information before the point-based single-stage detector, integrating the position, intensity and color information. Experiments results on the KITTI datasets using 3DSSD network as point encoder validate the effectiveness of our point painting method. On the KITTI car 3D detection test, our PP-3DSSD achieves 82.71% AP.

Openalex Percentile: Top 32%
Advanced Neural Network Applications
0.50
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PP-SSD: Point Painting Single Stage Detector for 3D Object Detection — Shiyou Chen, Yulin Liu, et al. · (2024) | TGRS Research Map | TGRS