A Bag of Features Approach for 3D Shape Retrieval

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Page 1. A Bag of Features Approach for. 3D Shape Retrieval. Janis Fehr, Alexander Streicher, Hans Burkhardt. Page 2. 2. Overview. “Bag of Features” approach.
A Bag of Features Approach for 3D Shape Retrieval Janis Fehr, Alexander Streicher, Hans Burkhardt

Overview

“Bag of Features” approach

- Search for 3D shapes in DB - extension from 2D - local 3D patches - invariant features (rotation) Evaluation: “Princeton Shape Benchmark” (PSB)

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First Steps ...

“Bag of Features” approach a)

interest point detection →

b)

extraction of local patches → spherical neighborhood + rotation invariant matching

c)

clustering



we use equidistant sampling

k-means



3

Harmonic Expansion of Spherical Patches Local spherical patches

Spherical Harmonic Domain

discrete: Rotations in SH

Power-Spectrum: 4

Full correlation in Spherical Harmonics correlation of signals on S²:

same correlation in SH: split into two rotations...

using...

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Full correlation in Spherical Harmonics

FFT

Orthoview 3D correlation matrix

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Clustering + Codebook Generation

CODEBOOK

Interest points: Equidistant sampling

k-means Clustering SH-correlation

Where each patch is sampled with expansions of different radii:

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Codebook Example

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Query

KNN-Classification by histogram-intersection

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Results

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