TY - GEN
T1 - Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
AU - Eigen, David
AU - Fergus, Rob
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/17
Y1 - 2015/2/17
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84973897611&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973897611&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2015.304
DO - 10.1109/ICCV.2015.304
M3 - Conference contribution
AN - SCOPUS:84973897611
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 2650
EP - 2658
BT - 2015 International Conference on Computer Vision, ICCV 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Computer Vision, ICCV 2015
Y2 - 11 December 2015 through 18 December 2015
ER -