Recent local
stereo matching algorithms based on an adaptive-weight strategy achieve
accuracy similar to global approaches. One of the major problems of
these algorithms is that they are computationally expensive and this
complexity increases proportionally to the window size. This paper
proposes a novel cost aggregation step with complexity independent of
the window size (i.e. O(1)) that outperforms state-of-the-art O(1)
methods. Moreover, compared to other O(1) approaches, our method does
not rely on integral histograms enabling aggregation using colour
images instead of grayscale ones. Finally, to improve the results of
the proposed algorithm a disparity refinement pipeline is also
proposed. The overall algorithm produces results comparable to those of
state-of-the-art stereo matching algorithms.
L. De-Maeztu, S. Mattoccia, A. Villanueva, R. Cabeza, "Linear stereo matching", 13th International Conference on Computer Vision (ICCV2011), November 6-13, 2011, Barcelona, Spain
[Pdf] [Supplementay_material] [Bibtex]
We propose a novel O(1) cost aggregation strategy that thanks to its symmetric nature outperforms stateof-the-art O(1) costs aggregation methods based integral histogram technique [2]. Compared to previous O(1) solutions, our proposal relies on a completely different approach inspired by the guided filter technique [1] that allows to aggregate costs using colour input stereo pairs (previous O(1) solutions aggregate costs using grayscale images because of memory footprint limitations). The symmetric and colour-based O(1) aggregation strategy proposed not only outperforms state-of-the-art O(1) algorithms, but it also produces on the Middlebury dataset [6] results comparable to non-O(1) adaptive-weight stereo matching (e.g. Adaptive Weights [3]). Thanks to the O(1) nature of our cost aggregation strategy, when the size of the input stereo pair grows, our method enables a dramatic improvement in execution time compared to AW stereo matching algorithm. We also propose a disparity refinement pipeline, that allows to obtain results comparable to top-performing stereo matching algorithms. The first stage of the pipeline removes large erroneous disparity assignments using a method similar to those described in [4]. Afterward, we enforce local consistency of the disparity field by means of the LC technique [5].
L. De-Maeztu, S. Mattoccia, A. Villanueva, R. Cabeza, "Linear stereo matching", 13th International Conference on Computer Vision (ICCV2011), November 6-13, 2011, Barcelona, Spain
[Pdf] [Supplementay_material] [Bibtex]
We propose a novel O(1) cost aggregation strategy that thanks to its symmetric nature outperforms stateof-the-art O(1) costs aggregation methods based integral histogram technique [2]. Compared to previous O(1) solutions, our proposal relies on a completely different approach inspired by the guided filter technique [1] that allows to aggregate costs using colour input stereo pairs (previous O(1) solutions aggregate costs using grayscale images because of memory footprint limitations). The symmetric and colour-based O(1) aggregation strategy proposed not only outperforms state-of-the-art O(1) algorithms, but it also produces on the Middlebury dataset [6] results comparable to non-O(1) adaptive-weight stereo matching (e.g. Adaptive Weights [3]). Thanks to the O(1) nature of our cost aggregation strategy, when the size of the input stereo pair grows, our method enables a dramatic improvement in execution time compared to AW stereo matching algorithm. We also propose a disparity refinement pipeline, that allows to obtain results comparable to top-performing stereo matching algorithms. The first stage of the pipeline removes large erroneous disparity assignments using a method similar to those described in [4]. Afterward, we enforce local consistency of the disparity field by means of the LC technique [5].
Linear stereo: results on the Middlebury dataset [1]
Linear Stereo vs AW[3]: measured execution time on a PC