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  • PhD Defense - Rongqi Qiu

    Tue, Apr 18, 2017 @ 10:00 AM - 12:00 PM

    Computer Science

    University Calendar

    PhD Candidate: Rongqi Qiu

    Committee: Ulrich Neumann (CS, chair), Panayiotis Georgiou (EE), Aiichiro Nakano (CS)

    Title: Geometric Modeling and Shape Analysis of 3D Point Clouds

    Time: April 18 (Tuesday) 10-12pm

    Room: SAL 322


    Automatic reconstruction of large-scale scenes from 3D point clouds has been a complex problem. It can be decomposed into two sub-problems, namely, primitives and parts. While primitives are regular geometric shapes, parts are relatively irregular and isolated objects.

    In primitive reconstruction, two systems under different scenarios are presented. The first one reconstructs pipe-runs from industrial site point clouds. The key idea is that by adopting statistical analysis over point normals, global similarities are discovered from raw data to guide primitive fitting, thus increasing robustness. The second system extracts pole-like objects from urban point clouds and posed multi-view images. The presented method takes advantage of the complementary information from 3D point clouds and 2D posed images to recover these objects.

    In part reconstruction, a modeling-by-recognition strategy is followed. Instead of directly meshing on a noisy scan, a similar object is retrieved from a pre-defined CAD model library. Then, geometric analysis is applied on the query and template point cloud to accomplish two tasks. The first one is to compute dense correspondences between query and template objects, thus making it possible to transfer real-world color to template models. The method segments both point clouds into parts consistently and then computes part-level correspondences. The dense mapping allows color or other parameter transfers. The second task is to segment an object into functional parts using a small set of pre-segmented template objects as examples. The main idea is to seek partial matches and transfer segmentation labels from examples to the input object. The resulting segmentation is a key step towards shape understanding.

    Location: Henry Salvatori Computer Science Center (SAL) - 322

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon


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