ODD-M3D: Object-Wise Dense Depth Estimation for Monocular 3-D Object Detection

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.