Abstract

We propose an approach for detecting ob jects in large-scale range datasets that combines bottom-up and top-down processes. In the bottom-up stage, fast-to-compute local descriptors are used to detect potential target ob jects. The ob ject hypotheses are verified after align- ment in a top-down stage using global descriptors that capture larger scale structure information. We have found that the combination of spin images and Extended Gaussian Images, as local and global descriptors respectively, provides a good trade-off between efficiency and accuracy. We present results on real outdoors scenes containing millions of scanned points and hundreds of targets. Our results compare favorably to the state of the art by being applicable to much larger scenes captured un- der less controlled conditions, by being able to detect ob ject classes and not specific instances, and by being able to align the query with the best matching model accurately, thus obtaining precise segmentation.