کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6949063 1451231 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A multi-scale fully convolutional network for semantic labeling of 3D point clouds
ترجمه فارسی عنوان
یک شبکه کاملا متشکل از چند مقیاس برای نشانه گذاری معانی ابر نقطه ای
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی
When classifying point clouds, a large amount of time is devoted to the process of engineering a reliable set of features which are then passed to a classifier of choice. Generally, such features - usually derived from the 3D-covariance matrix - are computed using the surrounding neighborhood of points. While these features capture local information, the process is usually time-consuming and requires the application at multiple scales combined with contextual methods in order to adequately describe the diversity of objects within a scene. In this paper we present a novel 1D-fully convolutional network that consumes terrain-normalized points directly with the corresponding spectral data (if available) to generate point-wise labeling while implicitly learning contextual features in an end-to-end fashion. This unique approach allows us to operate on unordered point sets with varying densities, without relying on expensive hand-crafted features; thus reducing the time needed for testing by an order of magnitude over existing approaches. Our method uses only the 3D-coordinates and three corresponding spectral features for each point. Spectral features may either be extracted from 2D-georeferenced images, as shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted directly for passive-derived point clouds, i.e. from multiple-view imagery. We train our network by splitting the data into square regions and use a pooling layer that respects the permutation-invariance of the input points. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6%. We ranked third place with a mean F1-score of 63.32%, surpassing the F1-score of the method with highest accuracy by 1.69%. In addition to labeling 3D-point clouds, we also show that our method can be easily extended to 2D-semantic segmentation tasks, with promising initial results.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 143, September 2018, Pages 191-204
نویسندگان
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