3D Point Cloud Scene Understanding with Semantic-Geometric Classes
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Solution Overview
Problem
Current 3D scene understanding methods are limited by the lack of annotated samples, particularly for under-represented semantic classes, leading to models that can only process a restricted number of object types and ignore a vast array of real-life objects.
Innovation Solution
A method that incorporates both semantic and geometric classes during training, using geometric descriptors like bounding box dimensions and volume to enhance the detection of objects, allowing models to recognize a broader range of objects by clustering based on shape and size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning functions are trained on limited semantic classes to ensure sufficient samples, then training reliability is improved, but the number of detectable object classes is reduced
Solution Approach 1:
The patent segments object detection into two independent classification tasks: semantic class classification and geometric class classification. This allows the model to handle under-represented semantic classes by using geometric descriptors as a separate classification dimension, thereby maintaining training reliability while expanding the number of detectable object types.
Solution Approach 2:
The patent introduces a new dimension of classification by incorporating geometric descriptors (shape, size, orientation) as a separate classification axis alongside semantic classes. This dimensional expansion enables the model to detect objects that were previously ignored due to insufficient semantic annotations, effectively increasing the number of detectable object classes without compromising training on well-represented semantic categories.
2Device complexity
If only semantic classes are used for training, then training simplicity is maintained, but detection accuracy for under-represented classes deteriorates
Solution Approach 1:
The patent merges semantic class information with geometric class information into a unified detection framework. By combining these two classification dimensions, the model achieves improved detection accuracy for under-represented classes while maintaining training simplicity through a unified loss function that handles both semantic and geometric classifications simultaneously.
3Adaptability or versatility
If geometric classes are added to semantic classes, then the number of detectable object types is increased, but training complexity increases
Solution Approach 1:
The patent implements a universal training framework that handles both semantic and geometric classifications through a single multi-functional loss function. This unified approach allows the model to learn both semantic meanings and geometric properties simultaneously, increasing the number of detectable object types while avoiding the need for separate training procedures that would increase complexity.
Data Source
AI summary
A computer-implemented method for machine learning a function configured to take as input a 3D point cloud of a real scene and to output localized representations each of a respective object of the real scene and, for each respective object, a class of the respective object among a predetermined set of classes. The method comprises obtaining a dataset of 3D point clouds annotated, for each 3D point cloud, with localized representations each of a respective object and, for each respective object, with a class of the respective object among the predetermined set of classes. The method includes training the function based on the obtained dataset. The predetermined set of classes comprises a plurality of semantic classes and a plurality of geometric classes. Such a method forms an improved solution for 3D scene understanding.