First approach to context-aware segmentation

In a first approach to context-aware segmentation (CAS), I have implemented a prototype that uses spherical harmonics descriptor and cord-and-angle histogram to estimate similarities between segments. This prototype decomposes all models in a given collection according to all others. Bellow is an example of a model extracted from the PRECISE Engineering Shape Benchmark and decomposed along with others from this benchmark using the CAS approach.

View of 3D model decomposed using CAS

Shape Segmentation with HFP

I have just combined the fitting primitives mesh clustering (HFP) algorithm proposed by Marco Attene in is paper "Hierarchical mesh segmentation based on fitting primitives" with the prototype that produces the spherical harmonics descriptor (SHA) and cord and angle histogram (CAH) for a 3D model. The result is an application that decomposes a model using the HFP approach and then computes for each segment the corresponding descriptors. This application produces a navigable VRML file containing the segmented model. By clicking on segments it is possible to see the descriptors for that segment and even the segment alone or a possible random sampling. To view an example based on a model extracted from the ESB click on screen-shot below.

Screen-shot of shape description page.

Computing the Cord and Angle Histogram

As a first step during the development of a prototype for computing descriptors for 3D shapes, I implemented the Cord and Angle Histogram (CAH) descriptor. Below are the results of the computation for the dog model I often use (m87 from the PSB). Additionally, you can see the corresponding VRML model showing the estimated cords.

Estimated cords colored according to length and angles with reference planes.

Computed cord and angle histograms