Volumetric Modeling Without Depth Sensors
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Solution Overview
Problem
Conventional volumetric modeling techniques relying on depth data face challenges in capturing complete and accurate models due to occlusions and limitations in capturing data from objects that are partially hidden or moving out of frame, leading to incomplete and unreliable models.
Innovation Solution
The development of methods and systems for volumetric modeling independent of depth data, utilizing machine learning processes to simulate cognitive analysis and predict object features and poses based on image data alone, allowing for the generation of complete and accurate models without the need for depth data capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If depth data capture devices are used for volumetric modeling, then model completeness can be improved, but device complexity and setup time increase
Solution Approach 1:
The patent extracts and removes the depth data capture device from the volumetric modeling system, achieving complete and accurate models using only 2D image data from standard cameras. This eliminates the complexity associated with depth sensors while maintaining model completeness through advanced image processing techniques.
Solution Approach 2:
The system makes standard 2D cameras perform the function previously requiring specialized depth sensors. By processing 2D images through machine learning algorithms, the system achieves volumetric modeling capabilities without needing dedicated depth capture devices, thereby reducing device complexity.
2Manufacturing precision
If depth data capture devices are used for volumetric modeling, then model accuracy can be improved, but setup time increases
Solution Approach 1:
The system performs preliminary calibration and machine learning model training offline, so that during actual volumetric modeling operations, accurate results are achieved without time-consuming setup. The pre-trained models enable rapid processing of 2D images into accurate volumetric representations.
Solution Approach 2:
The patent replaces the mechanical/optical depth capture system with a computational approach using machine learning algorithms. This substitution eliminates the need for complex hardware setup while maintaining high model accuracy through intelligent image analysis.
3Reliability
If depth data is used for volumetric modeling, then model reliability can be improved, but the system becomes more sensitive to occlusions
Solution Approach 1:
The system introduces machine learning algorithms as an intermediary between 2D image data and volumetric model generation. These algorithms can infer occluded regions and reconstruct complete 3D models even when parts of the object are hidden, making the system robust to occlusions while maintaining reliability.
Solution Approach 2:
The system creates multiple virtual views and representations from 2D images, using machine learning to generate complete volumetric models that fill in occluded areas. This copying approach allows the system to produce reliable models even when direct line-of-sight data is unavailable.
Data Source
AI summary
An illustrative image processing system determines that an object depicted in an image is an instance of an object type for which a machine learning model is available to the image processing system. In response to the determining that the object is the instance of the object type, the image processing system obtains pose data generated based on the machine learning model to represent how objects of the object type are capable of being posed. The image processing system generates a volumetric representation of the object in an estimated pose. The estimated pose is estimated independently of depth data for the object based on the image and the pose data. Corresponding methods and systems are also disclosed.