Object Model Adaptation Using Probability Maps for 3D Point Cloud Clustering
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Solution Overview
Problem
Existing methods for adapting object models to 3D point clouds often result in ambiguities and inaccuracies due to incorrect correspondences, particularly with repetitive structures, leading to misallocations and loss of information.
Innovation Solution
A method that generates a 3D point cloud from two images using a stereo method, applies clustering to identify point clusters, and uses a probability map (attention map) to correct misassignments, improving the object model adaptation by considering the likelihood of object presence and position, thereby reducing incorrect assignments and enhancing the quality of the point cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stereo methods are used to generate 3D point cloud from two images, then depth information can be obtained, but incorrect correspondences occur due to repetitive structures leading to misallocations
Solution Approach 1:
A probability map is introduced as an intermediary element between the stereo matching process and the final 3D point cloud generation. This probability map stores likelihood values for each pixel position, serving as a mediator that filters and weights correspondences before they are converted into 3D points, thereby reducing the impact of incorrect matches from repetitive structures
Solution Approach 2:
The probability map is computed in advance before the 3D point cloud generation and model adaptation processes. By pre-calculating the likelihood of correct correspondences for each pixel position, the system prepares filtering criteria beforehand, allowing subsequent steps to selectively use only high-confidence 3D points
2Manufacturing precision
If clustering method is applied to identify points belonging to cluster, then object regions can be segmented, but misallocated points remain affecting model adaptation
Solution Approach 1:
The system implements feedback by using the probability map information during model adaptation to identify and correct misallocated points. The probability values provide feedback signals that indicate which points are likely incorrectly assigned, allowing the system to iteratively refine the clustering and reassign doubtful points to appropriate clusters or mark them as outliers
3Measurement precision
If object model is superimposed on cluster for model adaptation, then object position can be determined, but ambiguities occur leading to false positive assignments
Solution Approach 1:
The system changes the parameter space by incorporating probability values as an additional dimension for evaluating model fits. Instead of relying solely on geometric matching parameters, the system weights model adaptation results by the probability map values, effectively changing how fit quality is measured and reducing false positives by considering correspondence reliability
Data Source
Figure 1
AI summary
The invention relates to a method for adapting an object model (OM) to a three-dimensional point cloud (PW). According to said method, the point cloud (PW) is generated from two images (B1, B2) by means of a stereo method (S1) and a clustering method (S2) is applied to the point cloud (PW) to identify points that pertain to respective clusters (CL), and then a model adaptation (S3) is carried out. At least one object model (OM) is superposed to at least one cluster (CL) and an optimum position of the object model (OM) relative to th cluster (CL) is determined. A correction (S4) of mismatches of points is carried out using the adapted object model (OM). A classifier (KL), which is trained with an exemplary object (BO), is used to draw up a probability map (AM) from at least one of the images (B1, B2). Based on said probability map (AM) a number and/or location probability of at least one object (O) that is similar to the exemplary object (BO) is determined in the image (B1, B2) and the probability map (AM) is taken into consideration during the clustering method (S2) and/or during model adaptation (S3).