Hierarchical Similarity Loss Enhanced Depth and Structural Fidelity in Monocular RGB-to-Depth Mapping with Adversarial Training

The conversion of monocular RGB images to depth maps is crucial in robotic applications. Current supervised learning approaches, dependent on high-quality RGB-Depth pairs, struggle with indoor environments characterized by multiple objects and fluctuating lighting, leading to inaccurate and unstable depth estimations. Additionally, commercial depth cameras on robots produce incomplete and light-sensitive depth maps, further degrading estimation quality. To surmount these issues, We created a comprehensive dataset of 9,600 RGB-Depth image pairs, capturing a range of indoor scenes under various lighting (dim, normal, and strong lighting), and interference conditions (local strong light interference, specular reflection interference, background similarity interference, and combinations of these factors). This dataset serves as a foundation for our proposed monocular RGB-to-depth mapping framework, which employs a hierarchical similarity loss to enhance the model’s structural feature learning from reference depth maps, improving the fidelity of estimated depth maps. We also integrated adversarial training and attention mechanisms to refine the depth consistency between the estimated and original depth maps. Experiments show our method surpasses current benchmarks in metrics like Threshold Accuracy, SILog, and MSE, demonstrating its robustness and potential for real-world applications, even with poor-quality inputs.