Static Depth Camera 3D Model Generation
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Solution Overview
Problem
Existing 3D object scanning systems are limited by the need for turntables, which restrict the size and type of objects that can be scanned, and fail to capture color or surface patterns, while also being complex and expensive to operate accurately.
Innovation Solution
A system using a substantially static depth camera captures depth images of objects rotated by a user, integrating them into a 3D model by tracking the object's position and orientation, and removing background and occluding hands to create a dense, accurate 3D model that includes color and surface details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a turntable is used to rotate the object at a known rate, then the 3D model accuracy is improved, but the device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical turntable system with a static depth camera system that captures multiple views as the user manually rotates the object. The system uses computer vision algorithms to track object position and orientation across frames, substituting mechanical precision requirements with computational tracking methods.
Solution Approach 2:
The patent creates a digital copy of the object's motion and position data through depth image sequences. By capturing and processing multiple depth images as the object rotates, the system reconstructs the 3D model without requiring the physical turntable mechanism, using digital tracking instead of mechanical rotation control.
2Measurement precision
If a turntable is used to scan the object, then the shape capture is improved, but the ability to scan different views and faces is restricted
Solution Approach 1:
The patent transitions from a static turntable with fixed rotation axis to a dynamic manual rotation system where the user can freely rotate the object in any direction and at any speed. The static depth camera captures depth images throughout the rotation, and computer vision algorithms track the object's changing position and orientation, enabling flexible multi-view scanning.
Solution Approach 2:
The system becomes universally applicable to objects of various sizes, shapes, and types that cannot be accommodated on a turntable. The manual rotation approach allows scanning of large objects, irregularly shaped objects, and objects with sensitive surfaces that would be damaged or difficult to secure on a rotating platform.
3Device complexity
If a static depth camera is used to capture depth images, then the system complexity is reduced, but the object position and orientation tracking becomes more difficult
Solution Approach 1:
The system uses feedback from consecutive depth images to track object position and orientation. By comparing depth data across multiple frames and using computer vision algorithms to identify consistent features, the system continuously updates its understanding of the object's pose, providing feedback that enables accurate tracking despite the static camera position.
Data Source
AI summary
Generating computer models of 3D objects is described. In one example, depth images of an object captured by a substantially static depth camera are used to generate the model, which is stored in a memory device in a three-dimensional volume. Portions of the depth image determined to relate to the background are removed to leave a foreground depth image. The position and orientation of the object in the foreground depth image is tracked by comparison to a preceding depth image, and the foreground depth image is integrated into the volume by using the position and orientation to determine where to add data derived from the foreground depth image into the volume. In examples, the object is hand-rotated by a user before the depth camera. Hands that occlude the object are integrated out of the model as they do not move in sync with the object due to re-gripping.