3D Point Cloud and BIM Component Retrieval for Subway Stations via Deep Learning

نویسندگان

چکیده

It is urgent to digitize the subway equipment detect changes in components station time. In this paper, we use 3D point cloud of as a benchmark, compare it with completed BIM model station, find out and retrieve components. First, obtained Xiamen Metro Station. Second, labeled 140 pairs matched component cloud. Third, constructed Siamese network which embedded triplet loss learn feature descriptors components, then Experimental results show that our proposed method realize retrieval environment.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Learning 3D Point Cloud Histograms

In this paper we show how using histograms based on the angular relationships between a subset of point normals in a 3D point Cloud can be used in a machine learning algorithm in order to recognize different classes of objects given by their 3D point clouds. This approach extends the work done by Gary Bradski at Willow Garage on point clouds recognition by applying a machine learning approach t...

متن کامل

Deep Learning on Point Sets for 3D Classification and Segmentation

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds and well respects the permutation invariance of point...

متن کامل

Criticality-based Model for Rehabilitating Subway Stations

According to the Canadian Urban Transit Association (CUTA), 140 Billion CAD is required to maintain, rehabilitate, and replace subway infrastructure between 2010 and 2014. The current practice adopted by transit authorities for prioritizing subway stations for rehabilitation is based on the station structural needs. While this classification is reflective of station condition, other factors, su...

متن کامل

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, these methods are computationally wasteful in attempt to predict 3D shapes, where information is rich only on the surfaces. In this paper, we propose a novel 3D generative modeling framework to efficien...

متن کامل

3D Interest Point Detection via Discriminative Learning

The task of detecting the interest points in 3D meshes has typically been handled by geometric methods. These methods, while designed according to human preference, can be ill-equipped for handling the variety and subjectivity in human responses. Different tasks have different requirements for interest point detection; some tasks may necessitate high precision while other tasks may require high...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Frontiers in artificial intelligence and applications

سال: 2022

ISSN: ['1879-8314', '0922-6389']

DOI: https://doi.org/10.3233/faia220566