@inproceedings{
yujian2024sparse,
title={Sparse Query Dense: Enhancing 3D Object Detection with Pseudo points},
author={Mo Yujian and Yan Wu and Junqiao Zhao and Hou zhenjie and weiquan Huang and Hu Yinghao and Jijun Wang and Jun Yan},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=42flABfqDT}
}
Current LiDAR-only 3D detection methods are limited by the sparsity of point clouds. The previous method used pseudo points generated by depth completion to supplement the LiDAR point cloud, but the pseudo points sample process was complex, and the distribution of pseudo points was uneven. Meanwhile, due to the imprecision of depth completion, the pseudo points suffer from noise and local structural ambiguity, which limit the further improvement of detection accuracy. This paper presents SQDNet, a novel framework designed to address these challenges. SQDNet incorporates two key components: the SQD, which achieves sparse-to-dense matching via grid position indices, allowing for rapid sampling of large-scale pseudo points on the dense depth map directly, thus streamlining the data preprocessing pipeline. And use the density of LiDAR points within these grids to alleviate the uneven distribution and noise problems of pseudo points. Meanwhile, the sparse 3D Backbone is designed to capture long-distance dependencies, thereby improving voxel feature extraction and mitigating local structural blur in pseudo points. The experimental results validate the effectiveness of SQD and achieve considerable detection performance for difficult-to-detect instances on the KITTI test.
Sparse Query Dense: Enhancing 3D Object Detection with Pseudo points
Mo Yujian Yan Wu Junqiao Zhao Hou zhenjie weiquan Huang Hu Yinghao Jijun Wang Jun Yan