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

David Eigen, Rob Fergus; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2650-2658

Abstract


In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional network that is able to adapt easily to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.

Related Material


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[bibtex]
@InProceedings{Eigen_2015_ICCV,
author = {Eigen, David and Fergus, Rob},
title = {Predicting Depth, Surface Normals and Semantic Labels With a Common Multi-Scale Convolutional Architecture},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}