engleski

Recognizing 3D Objects from a Limited Number of Views using Temporal Ensembles of Shape Functions

We consider the problem of 3D object recognition, assuming an application scenario involving a mobile robot equipped with an RGB-D camera. In order to simulate this scenario, we use a database of 3D objects and render partial point clouds representing depth views of an object. Using the rendered point clouds, we represent each object with an object descriptor called temporal ensemble of shape functions (TESF). We investigate leave-one-out 1-NN classification performance on the considered dataset depending on the number of views used to build TESF descriptors, as well as the possibility of matching the descriptors built using varying numbers of views. We establish the baseline by classifying individual view ESF descriptors. Our experiments suggest that classifying TESF descriptors outperforms individual ESF classification, and that TESF descriptors offer reasonable descriptivity even when very few views are used. The performance remains very good even if the query TESF and the nearest TESF are built using a differing number of views.

3D object recognition; shape functions; temporal ensembles of shape functions; object descriptors; RGB-D data; ESF; TESF

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