3D Scene Reconstruction with AI-Selected Point Cloud Frames
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Solution Overview
Problem
Existing methods for creating 3D models of scenes are time-consuming, computationally intensive, and often result in incomplete or inconsistent models due to the need for multiple perspectives and large data volumes, especially when dealing with irregularly shaped objects and wall openings.
Innovation Solution
A method involving AI systems to select and mark suitable frames for 3D model creation, segment point clouds into object groups, and apply network completion or object assignment methods based on frame suitability, reducing data processing and computational effort while ensuring high-quality model reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple perspectives are recorded to capture complete 3D models, then model quality and completeness are improved, but recording time and data volume increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-defining a set of reference perspectives and pre-calculating their geometric relationships. During recording, the system determines whether the current camera pose corresponds to any of these pre-defined perspectives, allowing rapid identification of useful frames without requiring comprehensive multi-perspective recording.
Solution Approach 2:
The invention applies partial action by selecting only the necessary subset of perspectives from the pre-defined reference set. Instead of recording from all possible angles, the system identifies and processes only those frames that correspond to the minimal set of reference perspectives needed to capture the essential 3D structure, reducing recording time while maintaining model quality.
2Manufacturing precision
If all recorded frames are processed to create comprehensive 3D models, then model completeness is improved, but computational effort and processing time increase significantly
Solution Approach 1:
The system performs preliminary computation by pre-calculating the geometric relationships between reference perspectives and establishing a framework for efficient frame selection. This preliminary setup enables rapid processing during execution, as the system only needs to determine which pre-defined reference perspectives correspond to current frames rather than analyzing all possible perspectives.
Solution Approach 2:
The invention applies partial action by processing only the subset of frames that correspond to the selected reference perspectives. Instead of processing all recorded frames, the system identifies and processes only those frames that provide essential information for the 3D model, significantly reducing computational effort while maintaining completeness.
3Loss of time
If irregularly shaped objects are modeled from single perspective, then recording time is reduced, but model accuracy and completeness deteriorate
Solution Approach 1:
The system performs preliminary preparation by pre-defining multiple reference perspectives that are geometrically distributed to capture objects from various angles. This preliminary setup allows the system to quickly identify which reference perspectives are relevant for each object, enabling accurate modeling of irregular shapes without requiring time-consuming multi-perspective recording.
Solution Approach 2:
The invention applies partial action by selecting only the necessary reference perspectives for each specific object based on its geometry and position. Instead of uniformly processing all perspectives, the system identifies the minimal subset of reference perspectives needed to accurately represent each irregular object, reducing recording time while maintaining modeling accuracy.
4Adaptability or versatility
If wall openings such as doors or windows are handled with object assignment methods, then known object databases can be utilized, but model quality deteriorates when database coverage is insufficient
Solution Approach 1:
The system segments the scene into different regions and objects, applying appropriate methods to each segment. For regular objects, object assignment methods with databases are used, while for irregular objects and wall openings, network completion methods are applied. This segmentation allows the system to leverage database coverage where available while maintaining model quality through alternative methods where databases are insufficient.
Solution Approach 2:
The invention applies local quality by selecting different processing methods based on the local characteristics of each object or region. Instead of using a single uniform method, the system adapts its approach locally - using object assignment for database-matched objects and network completion for irregular structures - thereby maintaining high model quality across diverse scene elements while utilizing database resources where applicable.
Data Source
AI summary
A method for creating a 3D model of a scene from a single frame of data points representing image and/or depth data is provided, comprising the steps of: segmenting the data points into a plurality of groups of data points, each group of data points having a high probability of representing a single object; determining, for each group of data points, which method from the group of network completion methods, object assignment methods is suitable for creating a 3D half model from the group of data points; creating a 3D half model from the group of data points according to the determined method, and merging the 3D half models into an overall 3D model.