10,587 research outputs found
Learning Regional Attraction for Line Segment Detection
This paper presents regional attraction of line segment maps, and hereby
poses the problem of line segment detection (LSD) as a problem of region
coloring. Given a line segment map, the proposed regional attraction first
establishes the relationship between line segments and regions in the image
lattice. Based on this, the line segment map is equivalently transformed to an
attraction field map (AFM), which can be remapped to a set of line segments
without loss of information. Accordingly, we develop an end-to-end framework to
learn attraction field maps for raw input images, followed by a squeeze module
to detect line segments. Apart from existing works, the proposed detector
properly handles the local ambiguity and does not rely on the accurate
identification of edge pixels. Comprehensive experiments on the Wireframe
dataset and the YorkUrban dataset demonstrate the superiority of our method. In
particular, we achieve an F-measure of 0.831 on the Wireframe dataset,
advancing the state-of-the-art performance by 10.3 percent.Comment: Accepted to IEEE TPAMI. arXiv admin note: text overlap with
arXiv:1812.0212
Holistically-Attracted Wireframe Parsing
This paper presents a fast and parsimonious parsing method to accurately and
robustly detect a vectorized wireframe in an input image with a single forward
pass. The proposed method is end-to-end trainable, consisting of three
components: (i) line segment and junction proposal generation, (ii) line
segment and junction matching, and (iii) line segment and junction
verification. For computing line segment proposals, a novel exact dual
representation is proposed which exploits a parsimonious geometric
reparameterization for line segments and forms a holistic 4-dimensional
attraction field map for an input image. Junctions can be treated as the
"basins" in the attraction field. The proposed method is thus called
Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed
method is tested on two benchmarks, the Wireframe dataset, and the YorkUrban
dataset. On both benchmarks, it obtains state-of-the-art performance in terms
of accuracy and efficiency. For example, on the Wireframe dataset, compared to
the previous state-of-the-art method L-CNN, it improves the challenging mean
structural average precision (msAP) by a large margin ( absolute
improvements) and achieves 29.5 FPS on single GPU ( relative
improvement). A systematic ablation study is performed to further justify the
proposed method.Comment: Accepted by CVPR 202
Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning
This article presents Holistically-Attracted Wireframe Parsing (HAWP), a
method for geometric analysis of 2D images containing wireframes formed by line
segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT)
field representation that encodes line segments using a closed-form 4D
geometric vector field. The proposed HAWP consists of three sequential
components empowered by end-to-end and HAT-driven designs: (1) generating a
dense set of line segments from HAT fields and endpoint proposals from
heatmaps, (2) binding the dense line segments to sparse endpoint proposals to
produce initial wireframes, and (3) filtering false positive proposals through
a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that
captures the co-occurrence between endpoint proposals and HAT fields for better
verification. Thanks to our novel designs, HAWPv2 shows strong performance in
fully supervised learning, while HAWPv3 excels in self-supervised learning,
achieving superior repeatability scores and efficient training (24 GPU hours on
a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe
parsing in out-of-distribution images without providing ground truth labels of
wireframes.Comment: Journal extension of arXiv:2003.01663; Accepted by IEEE TPAMI; Code
is available at https://github.com/cherubicxn/haw
DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients
Line segments are ubiquitous in our human-made world and are increasingly
used in vision tasks. They are complementary to feature points thanks to their
spatial extent and the structural information they provide. Traditional line
detectors based on the image gradient are extremely fast and accurate, but lack
robustness in noisy images and challenging conditions. Their learned
counterparts are more repeatable and can handle challenging images, but at the
cost of a lower accuracy and a bias towards wireframe lines. We propose to
combine traditional and learned approaches to get the best of both worlds: an
accurate and robust line detector that can be trained in the wild without
ground truth lines. Our new line segment detector, DeepLSD, processes images
with a deep network to generate a line attraction field, before converting it
to a surrogate image gradient magnitude and angle, which is then fed to any
existing handcrafted line detector. Additionally, we propose a new optimization
tool to refine line segments based on the attraction field and vanishing
points. This refinement improves the accuracy of current deep detectors by a
large margin. We demonstrate the performance of our method on low-level line
detection metrics, as well as on several downstream tasks using multiple
challenging datasets. The source code and models are available at
https://github.com/cvg/DeepLSD.Comment: Accepted at CVPR 202
Teaching, Analyzing, Designing and Interactively Simulating of Sliding Mode Control
This paper introduces an interactive methodology to analize, design, and simulate sliding model controllers for R2 linear systems. This paper reviews sliding mode basic concepts and design methodologies and describes an interactive tool which has been developed to support teaching in this field. The tool helps students by generating a nice graphical and interactive display of most relevant concepts. This fact can be used so that students build their own intuition about the role of different parameters in a sliding mode controller. Described application has been coded with Sysquake using an event-driven solver technique. The Sysquake allows using precise integration methods in real time and handling interactivity in a simple manner.Peer ReviewedPostprint (published version
Teaching, Analyzing, Designing and Interactively Simulating of Sliding Mode Control
This paper introduces an interactive methodology to analize, design, and simulate sliding model controllers for R2 linear systems. This paper reviews sliding mode basic concepts and design methodologies and describes an interactive tool which has been developed to support teaching in this field. The tool helps students by generating a nice graphical and interactive display of most relevant concepts. This fact can be used so that students build their own intuition about the role of different parameters in a sliding mode controller. Described application has been coded with Sysquake using an event-driven solver technique. The Sysquake allows using precise integration methods in real time and handling interactivity in a simple manner.Peer ReviewedPostprint (published version
Volumetric Wireframe Parsing from Neural Attraction Fields
The primal sketch is a fundamental representation in Marr's vision theory,
which allows for parsimonious image-level processing from 2D to 2.5D
perception. This paper takes a further step by computing 3D primal sketch of
wireframes from a set of images with known camera poses, in which we take the
2D wireframes in multi-view images as the basis to compute 3D wireframes in a
volumetric rendering formulation. In our method, we first propose a NEural
Attraction (NEAT) Fields that parameterizes the 3D line segments with
coordinate Multi-Layer Perceptrons (MLPs), enabling us to learn the 3D line
segments from 2D observation without incurring any explicit feature
correspondences across views. We then present a novel Global Junction
Perceiving (GJP) module to perceive meaningful 3D junctions from the NEAT
Fields of 3D line segments by optimizing a randomly initialized
high-dimensional latent array and a lightweight decoding MLP. Benefitting from
our explicit modeling of 3D junctions, we finally compute the primal sketch of
3D wireframes by attracting the queried 3D line segments to the 3D junctions,
significantly simplifying the computation paradigm of 3D wireframe parsing. In
experiments, we evaluate our approach on the DTU and BlendedMVS datasets with
promising performance obtained. As far as we know, our method is the first
approach to achieve high-fidelity 3D wireframe parsing without requiring
explicit matching.Comment: Technical report; Video can be found at https://youtu.be/qtBQYbOpVp
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