Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards 3D Scene Understanding by Referring Synthetic Models   Promising performance has been achieved for visual perception on the point
cloud. However, the current methods typically rely on labour-extensive
annotations on the scene scans. In this paper, we explore how synthetic models
alleviate the real scene annotation burden, i.e., taking the labelled 3D
synthetic models as reference for supervision, the neural network aims to
recognize specific categories of objects on a real scene scan (without scene
annotation for supervision). The problem studies how to transfer knowledge from
synthetic 3D models to real 3D scenes and is named Referring Transfer Learning
(RTL). The main challenge is solving the model-to-scene (from a single model to
the scene) and synthetic-to-real (from synthetic model to real scene's object)
gap between the synthetic model and the real scene. To this end, we propose a
simple yet effective framework to perform two alignment operations. First,
physical data alignment aims to make the synthetic models cover the diversity
of the scene's objects with data processing techniques. Then a novel
\textbf{convex-hull regularized feature alignment} introduces learnable
prototypes to project the point features of both synthetic models and real
scenes to a unified feature space, which alleviates the domain gap. These
operations ease the model-to-scene and synthetic-to-real difficulty for a
network to recognize the target objects on a real unseen scene. Experiments
show that our method achieves the average mAP of 46.08\% and 55.49\% on the
ScanNet and S3DIS datasets by learning the synthetic models from the ModelNet
dataset. Code will be publicly available.

16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin