Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
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Date
2022
Publication Type
Conference Paper
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yes
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Abstract
We present an automated machine learning approach for uncalibrated photometric stereo (PS). Our work aims at discovering lightweight and computationally efficient PS neural networks with excellent surface normal accuracy. Unlike previous uncalibrated deep PS networks, which are handcrafted and carefully tuned, we leverage differentiable neural architecture search (NAS) strategy to find uncalibrated PS architecture automatically. We begin by defining a discrete search space for a light calibration network and a normal estimation network, respectively. We then perform a continuous relaxation of this search space and present a gradient-based optimization strategy to find an efficient light calibration and normal estimation network. Directly applying the NAS methodology to uncalibrated PS is not straightforward as certain task-specific constraints must be satisfied, which we impose explicitly. Moreover, we search for and train the two networks separately to account for the Generalized Bas-Relief (GBR) ambiguity. Extensive experiments on the DiLiGenT dataset show that the automatically searched neural architectures performance compares favorably with the state-of-the-art uncalibrated PS methods while having a lower memory footprint.
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published
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Book title
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Journal / series
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Pages / Article No.
2304 - 2314
Publisher
IEEE
Event
22nd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022)
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Organisational unit
03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)
Notes
Conference lecture held on January 6, 2022