Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

The success of monocular depth estimation relies on large and diverse training sets. The experiments confirm that mixing data from complementary sources greatly improves monocular depth estimation.

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• 1 Due to the challenges associated with acquiring dense ground-truth depth across different environments at scale, a number of datasets with distinct characteristics and biases have emerged.
• 2 We develop tools that enable mixing multiple datasets during training, even if their annotations are incompatible.
• 3 In particular, we propose a robust training objective that is invariant to changes in depth range and scale, advocate the use of principled multi-objective learning to combine data from different sources, and highlight the importance of pretraining encoders on auxiliary tasks.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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