research-article

Learning Domain Invariant Features for Unsupervised Indoor Depth Estimation Adaptation

Authors:

Chuqiao ChenAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 9

Article No.: 290, Pages 1 - 23

https://doi.org/10.1145/3672397

Published: 23 September 2024 Publication History

Abstract

Predicting depth maps from monocular images has made an impressive performance in the past years. However, most depth estimation methods are trained with paired image-depth map data or multi-view images (e.g., stereo pair and monocular sequence), which suffer from expensive annotation costs and poor transferability. Although unsupervised domain adaptation methods are introduced to mitigate the reliance on annotated data, rare works focus on the unsupervised cross-scenario indoor monocular depth estimation. In this article, we propose to study the generalization of depth estimation models across different indoor scenarios in an adversarial-based domain adaptation paradigm. Concretely, a domain discriminator is designed for discriminating the representation from source and target domains, while the feature extractor aims to confuse the domain discriminator by capturing domain-invariant features. Further, we reconstruct depth maps from latent representations with the supervision of labeled source data. As a result, the feature extractor learned features possess the merit of both domain-invariant and low source risk, and the depth estimator can deal with the domain shift between source and target domains. We conduct the cross-scenario and cross-dataset experiments on the ScanNet and NYU-Depth-v2 datasets to verify the effectiveness of our method and achieve impressive performance.

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Cited By

Zhou YZhang ZSun WLiu XMin XZhai GCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Subjective and Objective Quality-of-Experience Assessment for 3D Talking HeadsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680964(6033-6042)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680964
Zhuang XZhou FLi ZSerra ESpezzano F(2024)MV-BART: Multi-view BART for Multi-modal Sarcasm DetectionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679570(3602-3611)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679570

Index Terms

Learning Domain Invariant Features for Unsupervised Indoor Depth Estimation Adaptation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
      2. Computer vision tasks
        Vision for robotics

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 9

September 2024

780 pages

EISSN:1551-6865

DOI:10.1145/3613681

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

Issue’s Table of Contents

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Publication History

Published: 23 September 2024

Online AM: 13 June 2024

Accepted: 25 May 2024

Revised: 18 May 2024

Received: 22 October 2023

Published in TOMM Volume 20, Issue 9

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National Key R & D Program of China
Guangdong Basic and Applied Basic Research Foundation
“Pioneer” and “Leading Goose” R & D Program of Zhejiang
Zhejiang Province Key Research and Development Program of China

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Cited By

Zhou YZhang ZSun WLiu XMin XZhai GCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Subjective and Objective Quality-of-Experience Assessment for 3D Talking HeadsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680964(6033-6042)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680964
Zhuang XZhou FLi ZSerra ESpezzano F(2024)MV-BART: Multi-view BART for Multi-modal Sarcasm DetectionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679570(3602-3611)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679570

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