Self-Supervised Depth Learning for Urban Scene Understanding
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As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth. It is natural for a learning agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, far away mountains don't move much; nearby trees move a lot. This natural relationship between the appearance of objects and their motion is a rich source of information about the world. In this work, we train a deep network, using fully automatic supervision, to predict relative scene depth from single images. The depth training images are automatically derived from simple videos of cars moving through a scene, using classic depth from motion techniques, and no human provided labels. We show that this pretext task of predicting depth from a single image induces features in the network that result in large improvements in a set of downstream tasks including semantic segmentation, joint road segmentation and car detection, and monocular (absolute) depth estimation, over a network trained from scratch. In particular, our pre-trained model outperforms an ImageNet counterpart for the monocular depth estimation task. Unlike work that analyzes video paired with additional information about direction of motion, our agent learns from "raw egomotion" video recorded from cars moving through the world. Unlike methods that require videos of moving objects, we neither depend on, nor are disrupted by, moving objects in the video. Indeed, we can benefit from predicting depth in the videos associated with various downstream tasks, showing that we can adapt to new scenes in an unsupervised manner to improve performance. By doing so, we achieve consistently better results over several different urban scene understanding tasks, obtaining results that are competitive with state-of-the-art method for monocular depth estimation.
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