Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ScrewNet: Category-Independent Articulation Model Estimation From Depth
  Images Using Screw Theory   Robots in human environments will need to interact with a wide variety of
articulated objects such as cabinets, drawers, and dishwashers while assisting
humans in performing day-to-day tasks. Existing methods either require objects
to be textured or need to know the articulation model category a priori for
estimating the model parameters for an articulated object. We propose ScrewNet,
a novel approach that estimates an object's articulation model directly from
depth images without requiring a priori knowledge of the articulation model
category. ScrewNet uses screw theory to unify the representation of different
articulation types and perform category-independent articulation model
estimation. We evaluate our approach on two benchmarking datasets and compare
its performance with a current state-of-the-art method. Results demonstrate
that ScrewNet can successfully estimate the articulation models and their
parameters for novel objects across articulation model categories with better
on average accuracy than the prior state-of-the-art method. Project webpage:
https://pearl-utexas.github.io/ScrewNet/

14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin