semantic-labels

To add semantic labels to USD scene objects, you apply SemanticsAPI class and label information directly to scene prims. This preserves USD composition patterns while preparing annotation-ready metadata for segmentation workflows.

USD semantic metadata assigns object classes and labels to scene prims for computer vision datasets. It prepares annotation-ready scenes that generate rendering outputs like semantic segmentation and ID maps for ground truth workflows.

Yes, USD composition directly affects semantic label inheritance behavior. When applying SemanticsAPI labels to prims, you must preserve existing USD composition patterns to maintain correct semantic label inheritance across the scene hierarchy.

Yes, you can use Python to apply SemanticsAPI labels during USD scene authoring. The process involves using validated API snippets to assign semantic classes and labels to scene prims across Python, C/C++, or composed scene tasks.

The best way to set up USD scenes for semantic segmentation is to apply SemanticsAPI class and label information to scene prims. This properly configures RenderProduct outputs and annotation-ready scene metadata for ground truth data generation.

USD semantic labels may fail for computer vision annotation if composition patterns are broken or invalid API snippets are used. You must preserve USD composition and maintain correct semantic label inheritance behavior to ensure accurate segmentation outputs.