Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information

doi:10.3390/s22072576

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information
	H. N. Hu; M. Zhu; M. Y. Li and K. L. Chan
刊名	Sensors
	2022
卷号	22 期号:7 页码:20
DOI	10.3390/s22072576
英文摘要	Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress. In contrast to LiDAR-based algorithms, the robustness of pseudo-LiDAR methods is still inferior. After conducting in-depth experiments, we realized that the main limitations are due to the inaccuracy of the target position and the uncertainty in the depth distribution of the foreground target. These two problems arise from the inaccurate depth estimation. To deal with the aforementioned problems, we propose two innovative solutions. The first is a novel method based on joint image segmentation and geometric constraints, used to predict the target depth and provide the depth prediction confidence measure. The predicted target depth is fused with the overall depth of the scene and results in the optimal target position. For the second, we utilize the target scale, normalized with the Gaussian function, as a priori information. The uncertainty of depth distribution, which can be visualized as long-tail noise, is reduced. With the refined depth information, we convert the optimized depth map into the point cloud representation, called a pseudo-LiDAR point cloud. Finally, we input the pseudo-LiDAR point cloud to the LiDAR-based algorithm to detect the 3D target. We conducted extensive experiments on the challenging KITTI dataset. The results demonstrate that our proposed framework outperforms various state-of-the-art methods by more than 12.37% and 5.34% on the easy and hard settings of the KITTI validation subset, respectively. On the KITTI test set, our framework also outperformed state-of-the-art methods by 5.1% and 1.76% on the easy and hard settings, respectively.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/66508]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	H. N. Hu,M. Zhu,M. Y. Li and K. L. Chan. Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information[J]. Sensors,2022,22(7):20.
APA	H. N. Hu,M. Zhu,&M. Y. Li and K. L. Chan.(2022).Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information.Sensors,22(7),20.
MLA	H. N. Hu,et al."Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information".Sensors 22.7(2022):20.