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標題: | OSSN:基於孿生網路對單樣本三維點雲之語義分割模型 OSSN: A One-shot Siamese Network for SemanticSegmentation of 3D Point Clouds |
作者: | Yi-Hsuan Huang 黃以瑄 |
指導教授: | 莊永裕(Yung-Yu Chuang) |
關鍵字: | 深度學習,點雲,語意分割,單樣本學習, Deep learning,Point cloud,Semantic segmentation,One-shot learning, |
出版年 : | 2019 |
學位: | 碩士 |
摘要: | 在這篇論文中,我們提出了一個新的網路架構OSSN,也是就我們所知第一個針對單樣本三維點雲做語意分割的研究。OSSN的核心概念在於比較各的資料點所學到的特徵之間的相關性,並且,我們假設同一個類別的資料點,在不同的場景之中,仍然可以保持某種程度的相似,而這個假設也在後續的實驗中得到驗證。
之所以提出這個新的問題設定有幾個主要的原因,首先,關於點雲的研究在近年來愈來愈受到重視,尤其在場景分析和自駕車等領域被大量使用,而目前準確率較高的方法大多需要用大量的資料來做訓練。但和平面影像相比,點雲不但資料蒐集不易,還非常難給正確的標記,所以我們希望可以使用像OSSN這樣的架構,來學到點與點之間的相關性,並解決輸入資料類別不平均的問題。 OSSN可以分成四個主要的部分:提取特徵,比較特徵之間的相似度,學習閾值和兩個損失函數。我們將OSSN使用在Stanford 3D semantic semantic parsing dataset上,得到了非常好的結果,並且,在論文中也會證明我們所設計的網路結構各部分對於正確率提升的效果。 In this work, we proposed a new network architecture named OSSN. As far as our best knowledge, OSSN is the first model that focuses on solving the problem of semantic segmentation with one-shot 3D point cloud. The core concept of OSSN is to compare the similarity between features of each individual points based on our hypothesis that points belong to the same class would still have high similarity even in different backgrounds. Such hypothesis is then confirmed by the obtained result. There are various motivations for our work. First, the study of 3D point cloud is getting more attentions recently, especially in the fields like scene analysis and applications related to autonomous vehicles. Second, the methods developed so far are still supervised learning that base on large amount of training samples which make these methods less applicable to many practical problems. Last but not least, even in the case that large amount of data is available, labeling them precisely may still require great labors, and in the worst case, the data might be imbalanced that complicate the overall procedure. Our OSSN is capable of solving, or alleviating the aforementioned problems by leveraging the similarity information between points. There are four major parts in OSSN: feature extraction, similarity comparison, the learned classification threshold, and two loss functions as the goodness criterion. Our OSSN achieves extraordinary performance in Stanford 3d semantic parsing dataset and in the work, we give viable explanations on the design philosophy and also why it works. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74917 |
DOI: | 10.6342/NTU201904151 |
全文授權: | 有償授權 |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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ntu-108-1.pdf 目前未授權公開取用 | 11.82 MB | Adobe PDF |
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