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arXiv · 1406.2283

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

David Eigen·Christian Puhrsch·Rob Fergus
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Abstract

Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation.

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David Eigen, Christian Puhrsch, Rob Fergus. 2014-06-09. Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. https://arxiv.org/abs/1406.2283

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