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In this paper we address three different computer vision tasks using a singlebasic architecture: depth prediction, surface normal estimation, and semanticlabeling. We use a multiscale convolutional network that is able to adapteasily to each task using only small modifications, regressing from the inputimage to the output map directly. Our method progressively refines predictionsusing a sequence of scales, and captures many image details without anysuperpixels or low-level segmentation. We achieve state-of-the-art performanceon benchmarks for all three tasks.

Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture