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3-D Object Recognition From Shape

This research focuses on improving 3-D object recognition by employing local features and surface matching techniques. The process involves generating spin signature-based local descriptors of shape that are pose-invariant, robust to clutter and occlusion. Recognition is achieved using Spherical Spin Images (SSI) to produce qualitative and quantitative recognition results, leading to a significant improvement. Over 4000 experiments have been conducted comparing different methods such as Standard Spin Images vs. Spherical Spin Images, and SSI with Random Projections vs. SI with PCA. The current research aims to enhance the recognition of well-defined object classes based on 3-D shape using statistical learning theory techniques like support vector machines and insights from human learning physiology.

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3-D Object Recognition From Shape

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  1. 3-D Object Recognition From Shape Salvador Ruiz Correa Department of Electrical Engineering

  2. The Problem 3-D Model (database) 3-D Scene

  3. Difficult Problem Cloud of points 3-D scanner Scene Pose Estimation Recognition Recovering

  4. Recognition Scene Model C A C’ B’ B A’ Surface Matching using local “features”

  5. Surface Matching • Search for candidate ‘point correspondences’ between the • the model and scene that are geometrically consistent. • (use distance measure in the “space of local features”). • Generate a set of potential matches (groups of 3 or more • correspondences). • Keep the match that produces the best alignment between • Surface meshes (rotation matrix and translation).

  6. Local Features

  7. Spin Signature Generation Local descriptors of shape Pose invariant Robust to clutter and occlusion

  8. Spherical Spin Images (SSI)

  9. Recognition using SSI

  10. Qualitative Results Scenes Recognition

  11. Results % Improvement • Tested four methods ~ 4000 experiments • Standard Spin Images vs Spherical Spin Images. • (SI vs SSI) • SI with PCA vs SSI with Random Projections • (SIC + SSIC)

  12. Current Research • Recognition of well defined classes of objects based on 3-D shape. • Use techniques from statistical learning theory (support vector machines) and knowledge from the physiology of learning in humans to achieve categorization, detection and recognition of shape classes.

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