Improving Image-Based Localization with Deep Learning: The Impact of the Loss Function
This work investigates the impact of the loss function on the performance of Neural Networks, in the context of a monocular, RGB-only, image localization task. A common technique used when regressing a camera's pose from an image is to formulate the loss as a linear combination of positional an...
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| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
28-04-2019
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | This work investigates the impact of the loss function on the performance of
Neural Networks, in the context of a monocular, RGB-only, image localization
task. A common technique used when regressing a camera's pose from an image is
to formulate the loss as a linear combination of positional and rotational mean
squared error (using tuned hyperparameters as coefficients). In this work we
observe that changes to rotation and position mutually affect the captured
image, and in order to improve performance, a pose regression network's loss
function should include a term which combines the error of both of these
coupled quantities. Based on task specific observations and experimental
tuning, we present said loss term, and create a new model by appending this
loss term to the loss function of the pre-existing pose regression network
`PoseNet'. We achieve improvements in the localization accuracy of the network
for indoor scenes; with decreases of up to 26.7% and 24.0% in the median
positional and rotational error respectively, when compared to the default
PoseNet. |
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| DOI: | 10.48550/arxiv.1905.03692 |