Monocular depth estimation using multi-dimensional dynamic convolution

Abstract

To address the issue of inaccurate prediction results and low accuracy at the object edges in existing self-supervised monocular depth estimation algorithms, this study proposes a self-supervised monocular depth estimation algorithm based on dynamic convolution (OE-Depth). The algorithm employs a multi-dimensional dynamic convolution feature extraction network to acquire more comprehensive feature representations, thereby enhancing the predictive capability. Additionally, the algorithm is optimized by integrating a triplet loss term and employing metric learning techniques to refine the networks performance at the object edges. Experimental evaluations conducted on the KITTI dataset validate the effectiveness of the proposed algorithm, demonstrating an optimization of the error class index by nearly 10%. Notably, the most stringent criterion < 1.25 achieves an accuracy of 0.908 for depth prediction.