An Application of Markov Random Fields to Range Sensing

James Diebel, Sebastian Thrun

2005 · 505 citations · 21 references

Concepts

TL;DR

This paper presents a highly successful application of Markov Random Fields to generate high‑resolution range images. A new generation of range sensors captures low‑resolution range data alongside registered high‑resolution camera images, and the MRF exploits co‑aligned range and color discontinuities to fuse these inputs, yielding high‑resolution, low‑noise range images. Using this MRF substantially improves performance over existing range imaging technology.

Abstract

This paper describes a highly successful application of MRFs to the problem of generating high-resolution range images. A new generation of range sensors combines the capture of low-resolution range images with the acquisition of registered high-resolution camera images. The MRF in this paper exploits the fact that discontinuities in range and coloring tend to co-align. This enables it to generate high-resolution, low-noise range images by integrating regular camera images into the range data. We show that by using such an MRF, we can substantially improve over existing range imaging technology.

References

21