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Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks

Abstract : Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular public datasets, proposes a categorization of the strategies employed by recent contributions, evaluates the performance of the current state-of-the-art, and discusses the remaining challenges and promising directions for future works.
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https://hal.archives-ouvertes.fr/hal-03264035
Contributor : José Mennesson Connect in order to contact the contributor
Submitted on : Thursday, June 17, 2021 - 6:14:22 PM
Last modification on : Tuesday, January 4, 2022 - 6:12:43 AM
Long-term archiving on: : Saturday, September 18, 2021 - 6:58:36 PM

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  • HAL Id : hal-03264035, version 1
  • ARXIV : 2105.11925

Citation

Sami Barchid, José Mennesson, Chaabane Djeraba. Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks. CBMI 2021 - Content-based Multimedia Indexing, Jun 2021, Lille / Virtual, France. ⟨hal-03264035⟩

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