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Uncertainty Estimation for Dense Stereo Matching using Bayesian Deep Learning

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Figure 2.1: Simplification of the matching process with known stereo calibration. (a) The relation of a stereo image pair with known intrinsic and relative orientation can be described via epipolar geometry, where O 0 , O 00 are the projection centres and
Figure 2.2: Relation between a stereo image pair, a cost volume and cost curves. If not otherwise specified, a cost volume refers to the left image of a planar rectified stereo image pair
Figure 2.3: Principle of convolutional and pooling operations. In this exemplary setup, a 2D convo- convo-lutional layer, consisting of multiple 3x3 filter kernels, is followed by an activation function to compute an intermediate feature map from a given i
Figure 2.4: Overview of the GC-Net architecture. Performing four major processing steps (feature extraction, cost volume construction, cost volume optimisation, disparity map extraction), GC-Net presented by Kendall et al
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Dense Stereo Matching Aleatoric Uncertainty Estimation Epistemic Uncertainty Estimation CNN-based Cost Volume Analysis Discussion CVA-Net Architecture Aleatoric Uncertainty Models Comparison to the Deterministic Baseline Discussion

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