Dense 3D data as delivered by stereo vision systems, modern laser scanners or timeof-flight cameras such as PMD is a key element for 3D scene understanding. Real-time high-level vision systems require a compact and explicit representation of that data which allows for efficient attention control, object detection, and reasoning. Because man-made environments are dominated by planar horizontal and vertical surfaces we approximate the three dimensional scenery by using sets of thin planar rectangles called Stixels. This medium level representation serves as input for further processing steps and applications. Using this novel representation those are not required to process the large amounts of raw 3D data individually. This reconstruction is addressed by means of a unified probabilistic approach. Dynamic programming allows to incorporate real-world constraints such as perspective ordering and delivers an optimal segmentation with respect to freespace and obstacle information. We present results for both stereo vision data and laser data. The real-time capable approach can also be used to fuse the information of multiple data sources.
Pfeiffer and Franke REF developed a unified probabilistic approach, also solved with dynamic programming, that considers the occurrence of multiple stixels per column.
Towards a Global Optimal Multi-Layer Stixel Representation of Dense 3D Data
BMVC
Computer Science