Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The ApolloScape Open Dataset for Autonomous Driving and its Application   Autonomous driving has attracted tremendous attention especially in the past
few years. The key techniques for a self-driving car include solving tasks like
3D map construction, self-localization, parsing the driving road and
understanding objects, which enable vehicles to reason and act. However, large
scale data set for training and system evaluation is still a bottleneck for
developing robust perception models. In this paper, we present the ApolloScape
dataset [1] and its applications for autonomous driving. Compared with existing
public datasets from real scenes, e.g. KITTI [2] or Cityscapes [3], ApolloScape
contains much large and richer labelling including holistic semantic dense
point cloud for each site, stereo, per-pixel semantic labelling, lanemark
labelling, instance segmentation, 3D car instance, high accurate location for
every frame in various driving videos from multiple sites, cities and daytimes.
For each task, it contains at lease 15x larger amount of images than SOTA
datasets. To label such a complete dataset, we develop various tools and
algorithms specified for each task to accelerate the labelling process, such as
3D-2D segment labeling tools, active labelling in videos etc. Depend on
ApolloScape, we are able to develop algorithms jointly consider the learning
and inference of multiple tasks. In this paper, we provide a sensor fusion
scheme integrating camera videos, consumer-grade motion sensors (GPS/IMU), and
a 3D semantic map in order to achieve robust self-localization and semantic
segmentation for autonomous driving. We show that practically, sensor fusion
and joint learning of multiple tasks are beneficial to achieve a more robust
and accurate system. We expect our dataset and proposed relevant algorithms can
support and motivate researchers for further development of multi-sensor fusion
and multi-task learning in the field of computer vision.

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The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
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The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th