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Transform metric depth images into inverse depth representations for enhanced depth perception in computer vision and AI art applications.
The MetricDepthToInverse
node is designed to transform metric depth images into their inverse depth representations. This conversion is particularly useful in various computer vision and AI art applications where inverse depth maps are preferred for their ability to emphasize closer objects more prominently than distant ones. By converting metric depth to inverse depth, this node allows for enhanced depth perception and visualization, which can be beneficial in tasks such as 3D reconstruction, augmented reality, and depth-based artistic effects. The node operates by applying a mathematical transformation to the input depth data, ensuring that the resulting inverse depth map is ready for further processing or visualization.
The depth
parameter is the sole input for the MetricDepthToInverse
node and it expects an image that represents the metric depth map. This input is crucial as it provides the raw depth data that will be transformed into an inverse depth map. The metric depth map typically contains values that represent the distance from the camera to the objects in the scene. The node processes this input to generate an inverse depth map, which can then be used for various applications. There are no specific minimum, maximum, or default values for this parameter, as it is dependent on the depth data provided by the user.
The output parameter depth
is an image that represents the inverse depth map derived from the input metric depth map. This output is significant as it provides a transformed view of the scene where closer objects are highlighted more than distant ones, which can be advantageous for depth-based analysis and visualization. The inverse depth map is particularly useful in scenarios where depth perception needs to be enhanced or when working with algorithms that require inverse depth data. The output is ready for further processing or direct use in applications that benefit from inverse depth representation.
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