NENet: Monocular Depth Estimation via Neural Ensembles

Depth estimation is getting a widespread popularity in the computer vision community, and it is still quite difficult to recover an accurate depth map using only one single RGB image. In this work, we observe a phenomenon that existing methods tend to exhibit asymmetric errors, which might open up a new direction for accurate and robust depth estimation. We carefully investigate into the phenomenon, and construct a two-level ensemble scheme, NENet, to integrate multiple predictions from diverse base predictors. The NENet forms a more reliable depth estimator, which substantially boosts the performance over base predictors. Notably, this is the first attempt to introduce ensemble learning and evaluate its utility for monocular depth estimation to the best of our knowledge. Extensive experiments demonstrate that the proposed NENet achieves better results than previous state-of-the-art approaches on the NYU-Depth-v2 and KITTI datasets. In particular, our method improves previous state-of-the-art methods from 0.365 to 0.349 on the metric RMSE on the NYU dataset. To validate the generalizability across cameras, we directly apply the models trained on the NYU dataset to the SUN RGB-D dataset without any fine-tuning, and achieve the superior results, which indicate its strong generalizability. The source code and trained models will be publicly available upon the acceptance.