3D Modeling Pose Correction via Object Movement Segmentation
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Solution Overview
Problem
Current 3D modeling technologies face challenges in accurately determining the type and state of object movement within images, particularly in segmenting objects into appropriate chunks and correcting pose estimates based on feature point changes, which affects the precision of sensor pose correction and object pose updating.
Innovation Solution
A processor-implemented method that determines the type of object movement by analyzing changes in feature points across frames, segments objects into chunks based on movement types, corrects movement types based on feature point changes, and updates sensor poses accordingly, using neural networks for feature point estimation and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If objects are segmented into chunks based on movement types, then the precision of pose estimation is improved, but the device complexity increases
Solution Approach 1:
The patent segments objects into multiple chunks based on their movement types, allowing independent pose estimation for each chunk. This segmentation enables more precise tracking of individual object parts while maintaining manageable computational complexity through modular processing of each chunk separately.
Solution Approach 2:
The patent dynamically adjusts the segmentation and pose estimation process based on the detected movement type of objects. By identifying different movement patterns (translation, rotation, deformation), the system adapts its processing approach to maintain precision while optimizing computational resources according to the actual object behavior.
2Reliability
If feature point changes are used to correct movement types, then the reliability of object state determination is improved, but the loss of information increases
Solution Approach 1:
The patent implements a feedback mechanism where feature point changes are continuously monitored and used to correct movement type determinations. The system compares expected feature point positions with actual positions, and uses this feedback to refine and correct the identified movement types, thereby improving reliability while preserving critical motion information.
3Manufacturing precision
If sensor pose is corrected based on object state, then the manufacturing precision of 3D models is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary segmentation and movement type classification of objects before conducting full pose estimation and 3D modeling. By pre-identifying object chunks and their movement characteristics, the system prepares data structures and parameters in advance, enabling faster and more precise 3D model generation without requiring complete reprocessing.
Data Source
AI summary
A processor-implemented method with three-dimensional (3D) modeling includes: determining a type of movement of an object detected in an image received from a sensor, based on a variability of a position of the object and a variability of a shape of the object; segmenting the object into one or more chunks each corresponding to a unit of movement, based on the determined type of movement; correcting the determined type of movement based on a change in position of one or more feature points of the one or more chunks of the object in the image; determining a state of the movement of the object based on the corrected type of the movement of the object; correcting a pose of the sensor based on a state of the object; and updating a pose for each of the one or more chunks of the object based on the determined state of the object and the corrected pose of the sensor.