Exploiting single image depth prediction for mono-stixel estimation

Fabian Brickwedde, Steffen Abraham, Rudolf Mester; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

The stixel-world is a compact and detailed environment representation specially designed for street scenes and automotive vision applications. A recent work proposes a monocamera based stixel estimation method based on the structure from motion principle and scene model to predict the depth and translational motion of the static and dynamic parts of the scene. In this paper, we propose to exploit the recent advantages in deep learning based single image depth prediction for mono-stixel estimation. In our approach, the mono-stixels are estimated based on the single image depth predictions, a dense optical flow field and semantic segmentation supported by the prior knowledge about the characteristic of typical street scenes. To provide a meaningful estimation, it is crucial to model the statistical distribution of all measurements, which is especially challenging for the single image depth predictions. Therefore, we present a semantic class dependent measurement model of the single image depth prediction derived from the empirical error distribution on the Kitti dataset.Our experiments on the Kitti-Stereo’2015 dataset show that we can significantly improve the quality of mono-stixel estimation by exploiting the single image depth prediction. Furthermore, our proposed approach is able to handle partly occluded moving objects as well as scenarios without translational motion of the camera.