Depth Image Segmentation via Iterative Closest Point Outlier Detection
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Solution Overview
Problem
Existing segmentation algorithms for depth maps are computationally complex and struggle to differentiate moving objects from the background in real-time, requiring substantial computational resources.
Innovation Solution
The use of an iterative closest point (ICP) algorithm to identify corresponding points between previous and current depth images, detecting outlying points which are labeled as moving objects, thereby segmenting moving objects without significant additional computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation algorithms are used to differentiate objects in depth maps, then segmentation accuracy is improved, but computational complexity increases substantially
Solution Approach 1:
The patent segments the depth map processing into two distinct phases: background modeling using point cloud registration to establish static scene geometry, and foreground detection by identifying deviations from the registered background. This segmentation allows the system to apply computationally intensive algorithms only where necessary (for background establishment) while using simpler operations for ongoing segmentation.
Solution Approach 2:
The system performs preliminary background modeling and point cloud registration before the actual segmentation operation. By pre-establishing the static scene structure through ICP registration of point clouds, the system prepares the background representation in advance, so that subsequent segmentation only requires comparing current depth data against the pre-computed background model, significantly reducing real-time computational requirements.
2Measurement precision
If traditional segmentation algorithms are applied to real-time depth maps, then object differentiation capability is improved, but processing speed decreases
Solution Approach 1:
The system performs preliminary background modeling using point cloud registration before real-time segmentation. By pre-computing the background point cloud and establishing the static scene geometry in advance, the system prepares all necessary background representations beforehand, enabling rapid foreground detection during real-time operation through simple deviation detection from the pre-modeled background.
Solution Approach 2:
The patent applies different processing qualities to different parts of the scene: the background undergoes intensive point cloud registration and modeling to achieve high precision, while the foreground detection uses simpler operations comparing current depth values against the established background model. This local differentiation of processing quality maintains segmentation accuracy while optimizing overall processing speed.
3Measurement precision
If machine-learning classifiers are used for segmentation, then segmentation accuracy is improved, but computational resources required increase
Solution Approach 1:
The patent replaces machine-learning classifier-based segmentation with a geometric approach using point cloud registration and outlier detection. Instead of using computationally intensive ML models to classify each pixel, the system uses ICP-based point cloud registration to model the background geometry and identifies foreground objects as geometric outliers, substituting a lighter computational approach that achieves similar segmentation accuracy with reduced resource consumption.
Data Source
AI summary
Moving object segmentation using depth images is described. In an example, a moving object is segmented from the background of a depth image of a scene received from a mobile depth camera. A previous depth image of the scene is retrieved, and compared to the current depth image using an iterative closest point algorithm. The iterative closest point algorithm includes a determination of a set of points that correspond between the current depth image and the previous depth image. During the determination of the set of points, one or more outlying points are detected that do not correspond between the two depth images, and the image elements at these outlying points are labeled as belonging to the moving object. In examples, the iterative closest point algorithm is executed as part of an algorithm for tracking the mobile depth camera, and hence the segmentation does not add substantial additional computational complexity.