Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry
  Perception with Adaptive Cross Weighted Loss from Monocular Videos   Self-supervised deep learning-based 3D scene understanding methods can
overcome the difficulty of acquiring the densely labeled ground-truth and have
made a lot of advances. However, occlusions and moving objects are still some
of the major limitations. In this paper, we explore the learnable occlusion
aware optical flow guided self-supervised depth and camera pose estimation by
an adaptive cross weighted loss to address the above limitations. Firstly, we
explore to train the learnable occlusion mask fused optical flow network by an
occlusion-aware photometric loss with the temporally supplemental information
and backward-forward consistency of adjacent views. And then, we design an
adaptive cross-weighted loss between the depth-pose and optical flow loss of
the geometric and photometric error to distinguish the moving objects which
violate the static scene assumption. Our method shows promising results on
KITTI, Make3D, and Cityscapes datasets under multiple tasks. We also show good
generalization ability under a variety of challenging scenarios.

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The categories of this paper are: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
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The categories of this paper are: eess
8
The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: physics