2,600 research outputs found
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation of 3D
shapes for recognition: should 3D shapes be represented with descriptors
operating on their native 3D formats, such as voxel grid or polygon mesh, or
can they be effectively represented with view-based descriptors? We address
this question in the context of learning to recognize 3D shapes from a
collection of their rendered views on 2D images. We first present a standard
CNN architecture trained to recognize the shapes' rendered views independently
of each other, and show that a 3D shape can be recognized even from a single
view at an accuracy far higher than using state-of-the-art 3D shape
descriptors. Recognition rates further increase when multiple views of the
shapes are provided. In addition, we present a novel CNN architecture that
combines information from multiple views of a 3D shape into a single and
compact shape descriptor offering even better recognition performance. The same
architecture can be applied to accurately recognize human hand-drawn sketches
of shapes. We conclude that a collection of 2D views can be highly informative
for 3D shape recognition and is amenable to emerging CNN architectures and
their derivatives.Comment: v1: Initial version. v2: An updated ModelNet40 training/test split is
used; results with low-rank Mahalanobis metric learning are added. v3 (ICCV
2015): A second camera setup without the upright orientation assumption is
added; some accuracy and mAP numbers are changed slightly because a small
issue in mesh rendering related to specularities is fixe
RF Localization in Indoor Environment
In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained
A point-in-polygon method based on a quasi-closest point
International audienceThis paper presents a numerically stable solution to a point-in-polygon problem by combining the orientation method and the uniform subdivision technique. We define first a quasi-closest point that can be locally found through the uniform subdivision cells, and then we provide the criteria for determining whether a point lies inside a polygon according to the quasi-closest point. For a large number of points to be tested against the same polygon, the criteria are employed to determine the inclusion property of an empty cell as well as a test point. The experimental tests show that the new method resolves the singularity of a test point on an edge without loss of efficiency. The GIS case study also demonstrates the capability of the method to identify which region contains a test point in a map
Towards General-Purpose Representation Learning of Polygonal Geometries
Neural network representation learning for spatial data is a common need for
geographic artificial intelligence (GeoAI) problems. In recent years, many
advancements have been made in representation learning for points, polylines,
and networks, whereas little progress has been made for polygons, especially
complex polygonal geometries. In this work, we focus on developing a
general-purpose polygon encoding model, which can encode a polygonal geometry
(with or without holes, single or multipolygons) into an embedding space. The
result embeddings can be leveraged directly (or finetuned) for downstream tasks
such as shape classification, spatial relation prediction, and so on. To
achieve model generalizability guarantees, we identify a few desirable
properties: loop origin invariance, trivial vertex invariance, part permutation
invariance, and topology awareness. We explore two different designs for the
encoder: one derives all representations in the spatial domain; the other
leverages spectral domain representations. For the spatial domain approach, we
propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding
to achieve loop origin invariance on simple polygons. For the spectral domain
approach, we develop NUFTspec based on Non-Uniform Fourier Transformation
(NUFT), which naturally satisfies all the desired properties. We conduct
experiments on two tasks: 1) shape classification based on MNIST; 2) spatial
relation prediction based on two new datasets - DBSR-46K and DBSR-cplx46K. Our
results show that NUFTspec and ResNet1D outperform multiple existing baselines
with significant margins. While ResNet1D suffers from model performance
degradation after shape-invariance geometry modifications, NUFTspec is very
robust to these modifications due to the nature of the NUFT.Comment: 58 pages, 20 figures, Accepted to GeoInformatic
Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries
We address 2D floorplan reconstruction from 3D scans. Existing approaches
typically employ heuristically designed multi-stage pipelines. Instead, we
formulate floorplan reconstruction as a single-stage structured prediction
task: find a variable-size set of polygons, which in turn are variable-length
sequences of ordered vertices. To solve it we develop a novel Transformer
architecture that generates polygons of multiple rooms in parallel, in a
holistic manner without hand-crafted intermediate stages. The model features
two-level queries for polygons and corners, and includes polygon matching to
make the network end-to-end trainable. Our method achieves a new
state-of-the-art for two challenging datasets, Structured3D and SceneCAD, along
with significantly faster inference than previous methods. Moreover, it can
readily be extended to predict additional information, i.e., semantic room
types and architectural elements like doors and windows. Our code and models
are available at: https://github.com/ywyue/RoomFormer.Comment: CVPR 2023 camera-ready. Project page:
https://ywyue.github.io/RoomForme
An automated system for electrical power symbol placement in electrical plan drawing
An electrical plan drawingâsometimes called a wiring diagram or electrical drawingâconsists of lines and symbols. Electrical plan drawings are prepared on 2D architectural floor plans using Computer-Aided Design and/or Drafting (CAD) programs. The placement/drawing of electrical power symbolsâsuch as sockets, lights, and switchesâis the first step of an electrical plan drawing. For this purpose, a smart system has been developed in this study to automatically draw/place electrical power symbols in appropriate locations. The system is based on the detection and classification/recognition of furnishing (decorative) symbols in the floor plans. We have created a furnishing symbol dataset drawing on dozens of architectural plan drawings that contain symbols of the most commonly used tools in floor plans, such as furniture, appliances, plumbing, doors, and windows. We used a Deep Convolutional Neural Network (D-CNN) with transfer learningâInception-v3 modelâ to classify furnishing symbols. We tested the model on 20 real floor plans and achieved a very satisfactory accuracy of 97.05% in furnishing symbol classification. The symbol drawing step, which is the first step of drawing the electrical plan, was automated using the work developed, thus achieving the aim of saving time and labour. Experimental studies show the effectiveness of the proposed automated system
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