2023
DOI: 10.48550/arxiv.2302.09789
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Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement

Abstract: Monocular depth estimation plays a fundamental role in computer vision. Due to the costly acquisition of depth ground truth, self-supervised methods that leverage adjacent frames to establish a supervisory signal have emerged as the most promising paradigms. In this work, we propose two novel ideas to improve self-supervised monocular depth estimation: 1) self-reference distillation and 2) disparity offset refinement. Specifically, we use a parameter-optimized model as the teacher updated as the training epoch… Show more

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