3D Object Recognition in Multimodal Scenes
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Solution Overview
Problem
Existing methods for recognizing and localizing 3D objects in scenes using machine vision systems are limited in their ability to handle arbitrary free-form objects, are not robust to noise and clutter, and are not suitable for real-time applications, especially when using both intensity images and 3D sensors.
Innovation Solution
A method that computes a model description of the object's surface features offline, allowing for efficient recognition and pose determination in multimodal scenes by optimizing consistency between 3D surface and intensity data, using feature points and descriptors to find matching pairs and refine poses, without requiring color, texture, or surface reflectance information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feature descriptor techniques are used for object recognition, then correspondence finding between scene and model points is enabled, but the method fails when the object has little distinctive shape or intensity information
Solution Approach 1:
The patent combines 3D geometric information from range images with intensity information from color images to create a unified object recognition system. This merging allows the system to recognize objects that lack distinctive intensity features by relying on their 3D geometric structure, while still utilizing intensity information when available for enhanced discrimination.
Solution Approach 2:
The patent introduces 3D geometric primitives as intermediary elements that bridge the gap between raw sensor data and object recognition. These primitives (planes, cylinders, spheres) serve as intermediate representations that capture essential geometric features, enabling recognition of objects with minimal distinctive information by decomposing complex objects into simpler geometric components.
2Ease of operation
If geometric primitive methods are used for object detection, then detection of simple geometric objects becomes easier due to intrinsic symmetries, but the methods cannot handle general free-form objects
Solution Approach 1:
The patent segments free-form objects into multiple geometric primitives through iterative detection and masking. By dividing complex free-form objects into simpler geometric components (planes, cylinders, spheres), the system can detect each primitive using its inherent symmetries and properties, then reconstruct the overall object shape from these segments, thereby enabling detection of general free-form objects while maintaining the computational advantages of primitive-based methods.
3Measurement precision
If range image based systems are used for 3D object recognition, then 3D information processing is enabled, but the systems are limited to single range image data and cannot incorporate other 3D sensor information
Solution Approach 1:
The patent creates a universal object recognition framework that can process 3D data from multiple sensor types (time-of-flight cameras, structured light systems, stereo vision, laser scanners) through a unified geometric primitive detection approach. The system uses sensor-agnostic 3D point cloud processing and geometric fitting algorithms that work equally well with data from different 3D sensing modalities, making the system multi-functional and adaptable to various sensor configurations.
4Measurement precision
If brute-force search is used in parameter space for pose determination, then comprehensive pose evaluation is achieved, but the computation time is too slow for real-time systems
Solution Approach 1:
The patent performs preliminary detection of geometric primitives and their poses before final object pose determination. By pre-identifying candidate geometric components and their transformations in the scene, the system narrows down the parameter space for subsequent optimization steps, avoiding exhaustive brute-force search while maintaining comprehensive pose evaluation through guided refinement of pre-computed candidate poses.
Data Source
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AI summary
The invention provides a method for recognizing instances of a 3D object in 3D scene data and scene intensity data and for determining the 3D poses of said instances comprising the following steps: (a) providing 3D object data and obtaining object intensity data; (b) providing 3D scene data and scene intensity data; (c) extracting scene feature points from the intensity data; (d) selecting at least one reference point from the 3D scene data; (e) computing, for each selected reference point, pose candidates for the 3D object under the assumption that said reference point is part of the 3D object by maximizing the number of extracted scene feature points that are consistent with the 3D object under the given pose candidate; (f) computing a set of filtered poses from the pose candidates.