Look Deeper into Depth : Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

21 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018
Subtitle of host publication15th European Conference, 2018, Proceedings
EditorsVittorio Ferrari, Martial Hebert, Cristian Sminchisescu, Yair Weiss
PublisherSpringer Verlag
Pages55-71
VolumePart XV
ISBN (Electronic)9783030012670
ISBN (Print)9783030012663
Publication statusPublished - Sep 2018

Publication series

NameLecture Notes in Computer Science
Volume11219
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title15th European Conference on Computer Vision (ECCV 2018)
PlaceGermany
CityMunich
Period8 - 14 September 2018

Abstract

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.

Research Area(s)

  • Attention loss, Monocular depth, Semantic labeling

Bibliographic Note

Research Unit(s) information for this record is provided by the author(s) concerned.

Citation Format(s)

Look Deeper into Depth : Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss. / Jiao, Jianbo; Cao, Ying; Song, Yibing; Lau, Rynson .

Computer Vision – ECCV 2018: 15th European Conference, 2018, Proceedings. ed. / Vittorio Ferrari; Martial Hebert; Cristian Sminchisescu; Yair Weiss. Vol. Part XV Springer Verlag, 2018. p. 55-71 (Lecture Notes in Computer Science; Vol. 11219).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review