Semantic segmentation

Theoretical basics

The main idea of our proposal is to create a semantic segmentation map to improve the quality of the disparity map by using this information in other steps of the pipeline. The semantic segmentation produced during this step can be used during the SGM optimization step to improve results. The methodology is resumed in the following equation:

$E(D) = \sum_{p}{C(p,Dp)} + \beta(\sum_{q \in Np}{P_{1}T(|D_{p} - D_{q}|=1)} + \sum_{q \in Np}{P_{2}T(|D_{p} - D_{q}|>1)})$ with $D$ the disparity image, $N_{p}$ the neighborhood of $p$ and $\beta$ stops optimization of a given path every time this path crosses a building edge.

The method available in Pandora is

ARNN for building semantic segmentation, made available by Plugin ARNN.

Configuration and parameters

Name	Description	Type	Default value	Available value	Required
segmentation_method	Semantic segmentation method	str	ARNN	ARNN	Yes

Optimization method:

ARNN: Configuration and parameters of Plugin ARNN for sub-parameters and configuration example.