TY - GEN
T1 - Viewpoint estimation—insights and model
AU - Divon, Gilad
AU - Tal, Ayellet
N1 - Publisher Copyright: © 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - This paper addresses the problem of viewpoint estimation of an object in a given image. It presents five key insights and a CNN that is based on them. The network’s major properties are as follows. (i) The architecture jointly solves detection, classification, and viewpoint estimation. (ii) New types of data are added and trained on. (iii) A novel loss function, which takes into account both the geometry of the problem and the new types of data, is propose. Our network allows a substantial boost in performance: from 36.1% gained by SOTA algorithms to 45.9%.
AB - This paper addresses the problem of viewpoint estimation of an object in a given image. It presents five key insights and a CNN that is based on them. The network’s major properties are as follows. (i) The architecture jointly solves detection, classification, and viewpoint estimation. (ii) New types of data are added and trained on. (iii) A novel loss function, which takes into account both the geometry of the problem and the new types of data, is propose. Our network allows a substantial boost in performance: from 36.1% gained by SOTA algorithms to 45.9%.
UR - http://www.scopus.com/inward/record.url?scp=85055692397&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-030-01264-9_16
DO - https://doi.org/10.1007/978-3-030-01264-9_16
M3 - منشور من مؤتمر
SN - 9783030012632
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 265
EP - 281
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Weiss, Yair
A2 - Hebert, Martial
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -