The KITTI Vision Benchmark Suite

We present a novel global stereo model designed for view interpolation. Unlike existing stereo models which only
output a disparity map, our model is able to output a 3D triangular mesh, which can be directly used for view
interpolation. To this aim, we partition the input stereo images into 2D triangles with shared vertices. Lifting
the 2D triangulation to 3D naturally generates a corresponding mesh. A technical difficulty is to properly split
vertices to multiple copies when they appear at depth discontinuous boundaries. To deal with this problem, we
formulate our objective as a two-layer MRF, with the upper layer modeling the splitting properties of the
vertices and the lower layer optimizing a region-based stereo matching. Experiments on the Middlebury and the
Herodion datasets demonstrate that our model is able to synthesize visually coherent new view angles with high
PSNR, as well as outputting high quality disparity maps which rank at the first place on the new challenging high
resolution iddlebury 3.0 benchmark.

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@InProceedings{Zhang_2015_ICCV,
author = {Zhang, Chi and Li, Zhiwei and Cheng,
Yanhua and Cai, Rui and Chao, Hongyang and Rui,
Yong},
title = {MeshStereo: A Global Stereo Model With
Mesh Alignment Regularization for View
Interpolation},
booktitle = {The IEEE International Conference on
Computer Vision (ICCV)},
month = {December},
year = {2015}
}