Method and Apparatus for Multi-Model Primitive Fitting based on Deep Geometric Boundary and Instance Aware Segmentation

Abstract

An image processing system includes an interface to transmit and receive data via a network, a processor connected to the interface, a memory storing an image processing program modules executable by the processor, wherein the image processing program causes the processor to perform operations. The operations include providing a point cloud of an image including objects into a segmentation network, segmenting point-wisely the point cloud into multiple classes of the objects and detecting boundaries of the objects using the segmentation network, wherein the segmentation network outputs a probability of associating primitive classes of the objects based on the segmented multiple classes and the segmented boundaries, verifying and refining the segmented multiple classes and the segmented boundaries using a predetermined fitting method, and correcting misclassification of the multiple classes of the objects by fitting the primitives to the multiple classes.

Description

FIELD OF THE INVENTION[0001]The present invention is generally related to an apparatus and method for multi-model primitive fitting, and more specifically to multi-model primitive fitting using deep geometric boundary and instance aware segmentation.BACKGROUND OF THE INVENTION[0002]The technical field of the related art is in reverse engineering by recognizing and fitting multi-model multi-instance geometric primitives (e.g., planes, cylinders, spheres, cones, etc.). The most classic solution to this problem is RANSAC-based method, which in practice often lead to inferior fitting results, due to a combination of multiple factors including noisy points (and therefore noisy normal estimation) and cluttered scene formed by multiple class and / or multiple instance of geometric primitives, which are well-known to impede RANSAC-based method's robustness. Other methods base on Hough Transform or global energy minimization, suffers similarly from the above challenges.SUMMARY OF THE INVENTION...

AI Technical Summary

Benefits of technology

BIASFit demonstrates superior performance over RANSAC-based methods in both qualitative and quantitative evaluations, providing robust and accurate fitting of geometric primitives in noisy and cluttered environments.

Problems solved by technology

As a multi-model multi-instance fitting problem, it has been tackled with different approaches including RANSAC, which however often fit inferior models in practice with noisy inputs of cluttered scenes.

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