Overview We tested our scan segmentation algorithm on a challenging dataset of cluttered scenes containing articulated wooden puppets. The dataset was acquired by a scanning system based on temporal stereo [1] (displayed on the left). The system consists of two cameras and a projector, and outputs a triangulated surface only in the areas that are visible to all three devices simultaneously. Each scan had around 225,000 points, which we subsampled to around 7,000 with standard software. Our goal was to segment the scenes into 4 classes --- puppet head, limbs, torso and background clutter. Point Features We used spin-images[2] as local point features. The spin-images were computed on the original high-resolution surface. The spin-images were of size 10 x 5 bins at two different resolutions. We scaled the spin-image values, and performed PCA to obtain 45 principal components. We added a bias feature (set to 1 for all points). Edge Features We use the surface links output by the scanner as edges in the MRF. We obtained the best results by using a single bias feature (set to 1 for all edges). |
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Results |
AMN |
SVM |
Voted-SVM |
Training set accuracy |
97.97% | 88.54% | 88.25% |
Testing set accuracy |
94.41% | 87.17% | 86.50% |
Precision on testing set* |
86.84% |
93.35% | 99.51% |
Recall on testing set* |
83.88% | 18.56% | 11.65% |
a) AMN Result |
b) SVM Result |
c) Voted-SVM result |
d) Result of running AMN without edges |