ODD-M3D: Object-Wise Dense Depth Estimation for Monocular 3D Object Detectionopen access
- Authors
- Park, Chanyeong; Kim, Heegwang; Jang, Junbo; Paik, Joonki
- Issue Date
- Feb-2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Convolutional neural network; Estimation; Feature extraction; Laser radar; Location awareness; Monocular 3D object detection; Object detection; Object detection; Point cloud compression; Three-dimensional displays
- Citation
- IEEE Transactions on Consumer Electronics, v.70, no.1, pp 646 - 655
- Pages
- 10
- Journal Title
- IEEE Transactions on Consumer Electronics
- Volume
- 70
- Number
- 1
- Start Page
- 646
- End Page
- 655
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/72952
- DOI
- 10.1109/TCE.2024.3366763
- ISSN
- 0098-3063
1558-4127
- Abstract
- Despite the significant benefits of low cost and scalability associated with monocular 3D object detection, accurately estimating depth from a single 2D image remains challenging due to the typical ill-posed nature of the problem. To address this issue, we propose a new method that improves depth estimation accuracy by randomly sampling object-wise points instead of relying on a single center point, which is a common practice in conventional methods. To generate the object-wise multiple reference points, we create a sampling space and obtain the ground truth by moving them from the sampling space to the object space. For this reason, the proposed approach is named ODD-M3D, which stands for Object-wise Dense Depth estimation for Monocular 3D object detection. In addition, we conduct an ablation study comparing LiDAR-guided and random sampling methods to identify the limitations of using point cloud data for image-based 3D object detection tasks. The proposed network achieved better performance by allowing for dense depth estimation instead of sparse depth estimation, which is typical in conventional networks. Authors
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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