Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Monocular Scene Flow Estimation   Scene flow estimation has been receiving increasing attention for 3D
environment perception. Monocular scene flow estimation -- obtaining 3D
structure and 3D motion from two temporally consecutive images -- is a highly
ill-posed problem, and practical solutions are lacking to date. We propose a
novel monocular scene flow method that yields competitive accuracy and
real-time performance. By taking an inverse problem view, we design a single
convolutional neural network (CNN) that successfully estimates depth and 3D
motion simultaneously from a classical optical flow cost volume. We adopt
self-supervised learning with 3D loss functions and occlusion reasoning to
leverage unlabeled data. We validate our design choices, including the proxy
loss and augmentation setup. Our model achieves state-of-the-art accuracy among
unsupervised/self-supervised learning approaches to monocular scene flow, and
yields competitive results for the optical flow and monocular depth estimation
sub-tasks. Semi-supervised fine-tuning further improves the accuracy and yields
promising results in real-time.

11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat