8,159 research outputs found
Automatic Structural Scene Digitalization
In this paper, we present an automatic system for the analysis and labeling
of structural scenes, floor plan drawings in Computer-aided Design (CAD)
format. The proposed system applies a fusion strategy to detect and recognize
various components of CAD floor plans, such as walls, doors, windows and other
ambiguous assets. Technically, a general rule-based filter parsing method is
fist adopted to extract effective information from the original floor plan.
Then, an image-processing based recovery method is employed to correct
information extracted in the first step. Our proposed method is fully automatic
and real-time. Such analysis system provides high accuracy and is also
evaluated on a public website that, on average, archives more than ten
thousands effective uses per day and reaches a relatively high satisfaction
rate.Comment: paper submitted to PloS On
Dense Piecewise Planar RGB-D SLAM for Indoor Environments
The paper exploits weak Manhattan constraints to parse the structure of
indoor environments from RGB-D video sequences in an online setting. We extend
the previous approach for single view parsing of indoor scenes to video
sequences and formulate the problem of recovering the floor plan of the
environment as an optimal labeling problem solved using dynamic programming.
The temporal continuity is enforced in a recursive setting, where labeling from
previous frames is used as a prior term in the objective function. In addition
to recovery of piecewise planar weak Manhattan structure of the extended
environment, the orthogonality constraints are also exploited by visual
odometry and pose graph optimization. This yields reliable estimates in the
presence of large motions and absence of distinctive features to track. We
evaluate our method on several challenging indoors sequences demonstrating
accurate SLAM and dense mapping of low texture environments. On existing TUM
benchmark we achieve competitive results with the alternative approaches which
fail in our environments.Comment: International Conference on Intelligent Robots and Systems (IROS)
201
Matterport3D: Learning from RGB-D Data in Indoor Environments
Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification
Manipulating Attributes of Natural Scenes via Hallucination
In this study, we explore building a two-stage framework for enabling users
to directly manipulate high-level attributes of a natural scene. The key to our
approach is a deep generative network which can hallucinate images of a scene
as if they were taken at a different season (e.g. during winter), weather
condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the
scene is hallucinated with the given attributes, the corresponding look is then
transferred to the input image while preserving the semantic details intact,
giving a photo-realistic manipulation result. As the proposed framework
hallucinates what the scene will look like, it does not require any reference
style image as commonly utilized in most of the appearance or style transfer
approaches. Moreover, it allows to simultaneously manipulate a given scene
according to a diverse set of transient attributes within a single model,
eliminating the need of training multiple networks per each translation task.
Our comprehensive set of qualitative and quantitative results demonstrate the
effectiveness of our approach against the competing methods.Comment: Accepted for publication in ACM Transactions on Graphic
Neural Discourse Structure for Text Categorization
We show that discourse structure, as defined by Rhetorical Structure Theory
and provided by an existing discourse parser, benefits text categorization. Our
approach uses a recursive neural network and a newly proposed attention
mechanism to compute a representation of the text that focuses on salient
content, from the perspective of both RST and the task. Experiments consider
variants of the approach and illustrate its strengths and weaknesses.Comment: ACL 2017 camera ready versio
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