A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction

In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image and its associated reflectance map into a number of surfaces which fit to various 3D surface models and have homogeneous reflectance (material) properties. In comparison to previous work on range image segmentation, the paper makes the following contributions. Firstly, it is aimed at generic natural scenes, indoor and outdoor, which are often much complexer than most of the existing experiments in the "polyhedra world". Natural scenes require the algorithm to automatically deal with multiple types (families) of surface models which compete to explain the data. Secondly, it integrates the range image with the reflectance map. The latter provides material properties and is especially useful for surface of high specularity, such as glass, metal, ceramics. Thirdly, the algorithm is designed by reversib...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Feng Han, Zhuowen Tu, Song Chun Zhu
• Keywords: Associated Reflectance Map, Computer Vision, Diffusion Markov Chain, ECCV 2002, Markov Chain, Markov Chain Monte, Markov Chain Search