The Sampling-Gaussian for stereo matching
The soft-argmax operation is widely adopted in neural network-based stereo matching methods to enable differentiable regression of disparity. However, network trained with soft-argmax is prone to being multimodal due to absence of explicit constraint to the shape of the probability distribution. Pre...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
08.10.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The soft-argmax operation is widely adopted in neural network-based stereo
matching methods to enable differentiable regression of disparity. However,
network trained with soft-argmax is prone to being multimodal due to absence of
explicit constraint to the shape of the probability distribution. Previous
methods leverages Laplacian distribution and cross-entropy for training but
failed to effectively improve the accuracy and even compromises the efficiency
of the network. In this paper, we conduct a detailed analysis of the previous
distribution-based methods and propose a novel supervision method for stereo
matching, Sampling-Gaussian. We sample from the Gaussian distribution for
supervision. Moreover, we interpret the training as minimizing the distance in
vector space and propose a combined loss of L1 loss and cosine similarity loss.
Additionally, we leveraged bilinear interpolation to upsample the cost volume.
Our method can be directly applied to any soft-argmax-based stereo matching
method without a reduction in efficiency. We have conducted comprehensive
experiments to demonstrate the superior performance of our Sampling-Gaussian.
The experimental results prove that we have achieved better accuracy on five
baseline methods and two datasets. Our method is easy to implement, and the
code is available online. |
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DOI: | 10.48550/arxiv.2410.06527 |