Lightweight Room Layout Estimation using a Single Panoramic Image

Due to limited computational capabilities of embedded systems, the trade-off relationship between algorithm performance and its computational complexity is crucial to apply deep learning models for new camera functions. In this paper, we suggest a lightweight deep representation for room layout esti...

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Bibliographic Details
Main Authors: Kil, Dayoung, Kim, Seong-Heum
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Computer architecture
Embedded systems
Estimation
Feature extraction
Layout
Lightweight Deep Learning
Logic gates
Neural Architecture Search
Room Layout Estimation
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Summary:Due to limited computational capabilities of embedded systems, the trade-off relationship between algorithm performance and its computational complexity is crucial to apply deep learning models for new camera functions. In this paper, we suggest a lightweight deep representation for room layout estimation using a single panoramic image. Based on HorizonNet [3], which typically requires a lot of computational resources at every step, we propose to replace the feature extraction networks of the residual network (ResNet) [6] and the long short-term memory (LSTM) [8] with a platform-aware neural architecture search model and an gated recurrent unit. In order to use fewer computational re-sources, the proposed architecture utilizes sampling-based optimization to select best hyperparameters. In our quantitative experiments, the lightweight network configured with the presented method uses only about 1/2 fewer parameters than the existing network. We also use real-world panorama images taken with RICOH THETA Z1 to validate its performance. In the qualitative experiments, no significant difference from the original model is observed with the same panorama inputs.
ISSN:2642-3901
DOI:10.23919/ICCAS55662.2022.10003901