Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Adversarially Robust and Domain Generalizable Stereo Matching by
  Rethinking DNN Feature Backbones   Stereo matching has recently witnessed remarkable progress using Deep Neural
Networks (DNNs). But, how robust are they? Although it has been well-known that
DNNs often suffer from adversarial vulnerability with a catastrophic drop in
performance, the situation is even worse in stereo matching. This paper first
shows that a type of weak white-box attacks can overwhelm state-of-the-art
methods. The attack is learned by a proposed stereo-constrained projected
gradient descent (PGD) method in stereo matching. This observation raises
serious concerns for the deployment of DNN-based stereo matching. Parallel to
the adversarial vulnerability, DNN-based stereo matching is typically trained
under the so-called simulation to reality pipeline, and thus domain
generalizability is an important problem. This paper proposes to rethink the
learnable DNN-based feature backbone towards adversarially-robust and domain
generalizable stereo matching by completely removing it for matching. In
experiments, the proposed method is tested in the SceneFlow dataset and the
KITTI2015 benchmark, with promising results. We compute the matching cost
volume using the classic multi-scale census transform (i.e., local binary
pattern) of the raw input stereo images, followed by a stacked Hourglass head
sub-network solving the matching problem. It significantly improves the
adversarial robustness, while retaining accuracy performance comparable to
state-of-the-art methods. It also shows better generalizability from simulation
(SceneFlow) to real (KITTI) datasets when no fine-tuning is used.

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The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th