Adaptive Semantic Fusion Framework for Unsupervised Monocular Depth Estimation

Unsupervised monocular depth estimation plays an important role in autonomous driving, and has been received considerable research attention in recent years. Nevertheless, numerous existing methods relying on photometric consistency are excessively susceptible to variations in illumination and suffer in the regions with strong reflection. To overcome this limitation, we propose a novel unsupervised depth estimation framework named ColorDepth, which forces the model to explore object semantic to infer depth. Specifically, we extract pixel-level semantic prior clues of objects using the semantic segmentation network. These priors and the original image are then adaptively fused into color data by a learnable parameter for depth estimation. The incorporation of semantics endows our model with the ability to perceive scene structure information. The fused data effectively alleviates the depth ambiguity within the same semantic block, leading to improved consistency and robustness in challenging scenarios. Extensive experiments on the KITTI and Make3D datasets show that our method surpasses the previous state-of-the-art methods even those supervised by additional constraints, and brings significant performance improvement particularly in the regions of high reflection.