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A platform for research: civil engineering, architecture and urbanism
Specific object finding in point clouds based on semantic segmentation and iterative closest point
Abstract This paper proposes an efficient and accurate process to find instances of a specific object in a point cloud scene where the object is unique, a member of a trained class, and the unique object's class is known. Object finding is essential for multiple applications like object detection, pose estimation, and asset tracking. Current template matching and object detection methods are slow or approximate feature matching to match objects, which generalize classes of objects and do not differentiate between specific objects. This paper proposes a specific-object finding methodology based on existing point cloud segmentation, fully convolutional geometric features, and a color-based iterative closest point algorithm. A point cloud template and scene are used, the latter is segmented, the resulting points matching the template's label are isolated, generating candidates. The candidate's geometric features are matched and compared with the point cloud template. The methodology results generate high accuracy for specific-object matching.
Highlights Methodology for finding specific objects in point clouds using algorithmic and machine learning for industrial applications. An automatic resampling framework to specify similarity thresholds. Network-based candidate generation using point cloud segmentation and convolutional geometric features. Identification of matches based on double color-based registration. A pipeline for finding specific objects that handles different point cloud densities.
Specific object finding in point clouds based on semantic segmentation and iterative closest point
Abstract This paper proposes an efficient and accurate process to find instances of a specific object in a point cloud scene where the object is unique, a member of a trained class, and the unique object's class is known. Object finding is essential for multiple applications like object detection, pose estimation, and asset tracking. Current template matching and object detection methods are slow or approximate feature matching to match objects, which generalize classes of objects and do not differentiate between specific objects. This paper proposes a specific-object finding methodology based on existing point cloud segmentation, fully convolutional geometric features, and a color-based iterative closest point algorithm. A point cloud template and scene are used, the latter is segmented, the resulting points matching the template's label are isolated, generating candidates. The candidate's geometric features are matched and compared with the point cloud template. The methodology results generate high accuracy for specific-object matching.
Highlights Methodology for finding specific objects in point clouds using algorithmic and machine learning for industrial applications. An automatic resampling framework to specify similarity thresholds. Network-based candidate generation using point cloud segmentation and convolutional geometric features. Identification of matches based on double color-based registration. A pipeline for finding specific objects that handles different point cloud densities.
Specific object finding in point clouds based on semantic segmentation and iterative closest point
Lopez, Daniel (author) / Haas, Carl (author) / Narasimhan, Sriram (author)
2023-10-02
Article (Journal)
Electronic Resource
English
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