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Resolution-sensitive self-supervised monocular absolute depth estimation
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AI
DOI：10.1007/s10489-024-05414-0.png)
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Abstract
En 中文
Depth estimation is an essential component of computer vision applications for environment perception, 3D reconstruction and scene understanding. Among the available methods, self-supervised monocular depth estimation is noteworthy for its cost-effectiveness, ease of installation and data accessibility. However, there are two challenges with current methods. Firstly, the scale factor of self-supervised monocular depth estimation is uncertain, which poses significant difficulties for practical applications. Secondly, the depth prediction accuracy for high-resolution images is still unsatisfactory, resulting in low utilization of computational resources. We propose a novel solution to address these challenges with three specific contributions. Firstly, an interleaved depth network skip-connection structure and a new depth network decoder are proposed to improve the depth prediction accuracy for high-resolution images. Secondly, a data vertical splicing module is suggested as a data enhancement method to obtain more non-vertical features and improve model generalization. Lastly, a scale recovery module is proposed to recover the accurate absolute depth without additional sensors, which solves the issue of uncertainty in the scale factor. The experimental results demonstrate that the proposed framework significantly improves the prediction accuracy of high-resolution images. In particular, the novel network structure and data vertical splicing module contribute significantly to this improvement. Moreover, in a scenario where the camera height is fixed and the ground is flat, the effect of scale recovery module is comparable to that achieved by using ground truth. Overall, the RSANet framework offers a promising solution to solve the existing challenges in self-supervised monocular depth estimation.
Keywords:
Monocular depth estimation
Self-supervision
Absolute depth
High resolution