Monocular depth estimation benefits greatly from learning based techniques. By studying the training data, we observe that the per-pixel depth values in existing datasets typically exhibit a long-tailed distribution. However, most previous approaches treat all the regions in the training data equally regardless of the imbalanced depth distribution, which restricts the model performance particularly on distant depth regions. In this paper, we investigate the long tail property and delve deeper into the distant depth regions (i.e. the tail part) to propose an attention-driven loss for the network supervision. In addition, to better leverage the semantic information for monocular depth estimation, we propose a synergy network to automatically learn the information sharing strategies between the two tasks. With the proposed attention-driven loss and synergy network, the depth estimation and semantic labeling tasks can be mutually improved. Experiments on the challenging indoor dataset show that the proposed approach achieves state-of-the-art performance on both monocular depth estimation and semantic labeling tasks.
|Title of host publication||Computer Vision – ECCV 2018|
|Subtitle of host publication||15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XV|
|Editors||Yair Weiss, Vittorio Ferrari, Cristian Sminchisescu, Martial Hebert|
|Number of pages||17|
|Publication status||Published - 7 Oct 2018|
|Event||15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany|
Duration: 8 Sept 2018 → 14 Sept 2018
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||15th European Conference on Computer Vision, ECCV 2018|
|Period||8/09/18 → 14/09/18|
Bibliographical noteFunding Information:
Acknowledgments. This work is partially supported by the Hong Kong PhD Fellowship Scheme (HKPFS) from the RGC of Hong Kong.
© Springer Nature Switzerland AG 2018.
- Attention loss
- Monocular depth
- Semantic labeling
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)