Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-View Transformer for 3D Visual Grounding   The 3D visual grounding task aims to ground a natural language description to
the targeted object in a 3D scene, which is usually represented in 3D point
clouds. Previous works studied visual grounding under specific views. The
vision-language correspondence learned by this way can easily fail once the
view changes. In this paper, we propose a Multi-View Transformer (MVT) for 3D
visual grounding. We project the 3D scene to a multi-view space, in which the
position information of the 3D scene under different views are modeled
simultaneously and aggregated together. The multi-view space enables the
network to learn a more robust multi-modal representation for 3D visual
grounding and eliminates the dependence on specific views. Extensive
experiments show that our approach significantly outperforms all
state-of-the-art methods. Specifically, on Nr3D and Sr3D datasets, our method
outperforms the best competitor by 11.2% and 7.1% and even surpasses recent
work with extra 2D assistance by 5.9% and 6.6%. Our code is available at
https://github.com/sega-hsj/MVT-3DVG.

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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: supr-con