Hybrid 3D Reconstruction with Semantic Segmentation for Unknown Objects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for 3D object reconstruction in extended reality environments are time-consuming, expensive, and lack flexibility, and scanning-based approaches can be incomplete or computationally expensive, while database-based methods are limiting when encountering unknown objects.
Innovation Solution
A hybrid approach combining semantic segmentation and reconstruction, where objects are segmented, compared against a registered set, and new 3D models are registered and integrated, allowing for efficient generation and updating of 3D models for multiple instances of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scanning-based approaches are used for 3D object reconstruction, then complete 3D models can be obtained, but the process becomes computationally expensive and time-consuming
Solution Approach 1:
The system performs preliminary actions by comparing objects against a database of registered objects before performing full 3D reconstruction. When a match is found, the pre-existing 3D model is used directly, avoiding the need for time-consuming scanning and reconstruction processes while still achieving complete and accurate 3D models
Solution Approach 2:
Instead of creating new 3D models through expensive scanning for every object, the system creates copies of existing 3D models from the database that have been registered and verified. This copying approach maintains model completeness while dramatically reducing reconstruction time and computational cost
2Productivity
If database-based methods are used for 3D object reconstruction, then reconstruction time is reduced, but the method becomes limiting when encountering unknown objects not in the database
Solution Approach 1:
The system implements feedback by continuously comparing detected objects against the database and using the results to determine the appropriate action. When unknown objects are detected (no match found), the feedback mechanism triggers full 3D reconstruction and database registration, ensuring the system adapts to new objects while maintaining fast reconstruction for known objects
Solution Approach 2:
The system dynamically adjusts its reconstruction approach based on object recognition results. For known objects, it uses the fast database lookup approach; for unknown objects, it transitions to the more comprehensive scanning and reconstruction approach. This dynamic adaptation optimizes both speed and versatility across different scenarios
3Manufacturing precision
If manual 3D model generation is used, then model accuracy is high, but the process is expensive and time-consuming
Solution Approach 1:
The system creates accurate 3D models by copying and registering objects from the database rather than performing manual generation for each instance. This approach maintains high model accuracy through proven database models while dramatically improving productivity by eliminating repetitive manual creation processes
Solution Approach 2:
High-quality 3D models are created in advance and stored in the database through preliminary manual or scanned generation. When needed, these pre-created models are quickly retrieved and applied, maintaining accuracy while improving efficiency by performing the labor-intensive work beforehand
Data Source
AI summary
Techniques and systems are provided for image processing. For instance, a process can include generating a segmentation class for a first object in a received first image; generating a first three-dimensional (3D) model of the first object; comparing the first object against a set of registered objects based on the segmentation class to determine that the first object is not in the set of registered objects; registering the first 3D model of the first object based on the determination that the first object is not in the set of registered objects; and outputting the first 3D model of the first object.