An integrated framework for 3 d modeling, object detection, and pose estimation from point-clouds
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This paper introduces a novel algorithm for simultaneous 3D modeling, object detection, and pose estimation from unordered point-clouds, addressing challenges like clutter and occlusion. The algorithm constructs an initial model through pairwise registration and updates it using a model growing technique, ultimately detecting object instances and estimating their poses without requiring prior information. Tested on the University of Western Australia dataset, the algorithm demonstrates high accuracy in modeling, detection, and pose estimation.
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An integrated framework for 3 d modeling, object detection, and pose estimation from point-clouds
- 1. AN INTEGRATED FRAMEWORK FOR 3-D MODELING, OBJECT DETECTION, AND POSE ESTIMATION FROM POINT-CLOUDS ABSTRACT 3-D modeling, object detection, and pose estimationare three of the most challenging tasks in the area of 3-D computervision. This paper presents a novel algorithm to performthese tasks simultaneously from unordered point-clouds. Given aset of input point-clouds in the presence of clutter and occlusion,an initial model is first constructed by performing pair- wiseregistration between any two point-clouds. The resulting models then updated from the remaining point-clouds using a novelmodel growing technique. Once the final model is reconstructed,the instances of the object are detected and the poses of itsinstances in the scenes are estimated. This algorithm is automatic,model free, and does not rely on any prior information about theobjects in the scene. The algorithm was comprehensively tested onthe University of Western Australia data set. Experimental resultsshow that our algorithm achieved accurate modeling, detection,and pose estimation performance.