Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Does depth estimation help object detection?   Ground-truth depth, when combined with color data, helps improve object
detection accuracy over baseline models that only use color. However, estimated
depth does not always yield improvements. Many factors affect the performance
of object detection when estimated depth is used. In this paper, we
comprehensively investigate these factors with detailed experiments, such as
using ground-truth vs. estimated depth, effects of different state-of-the-art
depth estimation networks, effects of using different indoor and outdoor RGB-D
datasets as training data for depth estimation, and different architectural
choices for integrating depth to the base object detector network. We propose
an early concatenation strategy of depth, which yields higher mAP than previous
works' while using significantly fewer parameters.

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The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: nlin
14
The categories of this paper are: math-ph