Out-of-Distribution Detection for Monocular Depth Estimation
Julia Hornauer Adrian Holzbock Vasileios Belagiannis
In monocular depth estimation, uncertainty estimation approaches mainly
target the data uncertainty introduced by image noise. In contrast to prior
work, we address the uncertainty due to lack of knowledge, which is relevant
for the detection of data not represented by the training distribution, the
so-called out-of-distribution (OOD) data. Motivated by anomaly detection, we
propose to detect OOD images from an encoder-decoder depth estimation model
based on the reconstruction error. Given the features extracted with the fixed
depth encoder, we train an image decoder for image reconstruction using only
in-distribution data. Consequently, OOD images result in a high reconstruction
error, which we use to distinguish between in- and out-of-distribution samples.
We built our experiments on the standard NYU Depth V2 and KITTI benchmarks as
in-distribution data. Our post hoc method performs astonishingly well on
different models and outperforms existing uncertainty estimation approaches
without modifying the trained encoder-decoder depth estimation model.