Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Class-agnostic Reconstruction of Dynamic Objects from Videos   We introduce REDO, a class-agnostic framework to REconstruct the Dynamic
Objects from RGBD or calibrated videos. Compared to prior work, our problem
setting is more realistic yet more challenging for three reasons: 1) due to
occlusion or camera settings an object of interest may never be entirely
visible, but we aim to reconstruct the complete shape; 2) we aim to handle
different object dynamics including rigid motion, non-rigid motion, and
articulation; 3) we aim to reconstruct different categories of objects with one
unified framework. To address these challenges, we develop two novel modules.
First, we introduce a canonical 4D implicit function which is pixel-aligned
with aggregated temporal visual cues. Second, we develop a 4D transformation
module which captures object dynamics to support temporal propagation and
aggregation. We study the efficacy of REDO in extensive experiments on
synthetic RGBD video datasets SAIL-VOS 3D and DeformingThings4D++, and on
real-world video data 3DPW. We find REDO outperforms state-of-the-art dynamic
reconstruction methods by a margin. In ablation studies we validate each
developed component.

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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin