Efficient Depth Completion Using Learned Bases
- URL: http://arxiv.org/abs/2012.01110v1
- Date: Wed, 2 Dec 2020 11:57:37 GMT
- Title: Efficient Depth Completion Using Learned Bases
- Authors: Yiran Zhong, Yuchao Dai, Hongdong Li
- Abstract summary: We propose a new global geometry constraint for depth completion.
By assuming depth maps often lay on low dimensional subspaces, a dense depth map can be approximated by a weighted sum of full-resolution principal depth bases.
- Score: 94.0808155168311
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a new global geometry constraint for depth
completion. By assuming depth maps often lay on low dimensional subspaces, a
dense depth map can be approximated by a weighted sum of full-resolution
principal depth bases. The principal components of depth fields can be learned
from natural depth maps. The given sparse depth points are served as a data
term to constrain the weighting process. When the input depth points are too
sparse, the recovered dense depth maps are often over smoothed. To address this
issue, we add a colour-guided auto-regression model as another regularization
term. It assumes the reconstructed depth maps should share the same nonlocal
similarity in the accompanying colour image. Our colour-guided PCA depth
completion method has closed-form solutions, thus can be efficiently solved and
is significantly more accurate than PCA only method. Extensive experiments on
KITTI and Middlebury datasets demonstrate the superior performance of our
proposed method.
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