This paper aims to design better generalized monocular depth estimation models. To this end, we have conducted quantitative analysis and discovered two important insights. First, the Simulation Correlation phenomenon, often found in long-tailed classification problem, also exists in monocular depth estimation, indicating that the imbalanced depth distribution in training data may be the cause of limited generalization ability. Second, the imbalanced and long-tailed distribution of depth values extends beyond the dataset scale, but also manifests within each individual image, further exacerbating the difficulty of monocular depth estimation. Motivated by the above findings, we propose the Distance-aware Multi-Expert (DME) depth estimation model. Unlike prior methods that handle different depth range indiscriminately, DME adopts a divide-and-conquer philosophy, where each expert is responsible for depth estimation of regions within a specific depth range. As such, the depth distribution seen by each expert is more uniform and can be more easily predicted. A router network is further designed and learned to stitch the prediction of all experts into the final depth map. Experiments show that DME achieves state-of-the-art performance on both NYU-Depth v2 and KITTI, and also delivers favorable zero-shot generalization capability on unseen datasets.

DME: Unveiling the Bias for Better Generalized Monocular Depth Estimation

Main Track - Computer Vision

technical paper

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Songsong Yu

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DME: Unveiling the Bias for Better Generalized Monocular Depth Estimation

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