樂高公司持續推出新的系列及不同種類的磚，這樣的多樣性也使得樂高深受大人小孩的喜愛，對於一些新推出的樂高系列，相對較少有研究進行探討，但也會有與其他領域，像是拼貼、排列、像素化問題等或與樂高研究議題相關的地方。像素化藝術方面的研究一直以來都非常受歡迎，但這樣的風格就是每個區域都為方形，對於一些圖形較圓滑的地方無法很好地表現出來。本研究的樂高豆豆系列則是將畫素以實體元件表現出來，而除了方形的磚，也有一些帶有較圓滑邊緣的磚。但當我們要以人工的方式去拼一個形狀時，在磚形狀及顏色的選擇上，就會花費非常多的時間，如果要拼的東西越大，所花費的時間也就更久，會浪費許多勞力和時間。
為了解決這些問題，本研究首先嘗試將編碼器-解碼器架構的網路與二維樂高平板磚建構相結合。以樂高平板磚中的方形磚及帶圓滑邊緣的磚轉換為圖片作為輸入，並透過給定損失函數，將現有的照片馬賽克神經網路研究延伸至樂高平板磚的組合問題。同時，我們也針對輸入圖形及生成的圖片，做一系列的比較及分析，證明此系統的有效性。

LEGO continues to release new series and different types of bricks, which has made it popular with both adults and children. However, there has been relatively little research on some of the newer Lego series, but there are also related studies in other areas such as puzzles, arrangements, pixelation, and other Lego research topics. Pixel art research has always been very popular, but this style is characterized by square regions, which makes it difficult to represent smoother shapes. The Lego Dots series studied in this research is just like expressing pixels as physical components, and in addition to square bricks, there are also bricks with smoother edges. However, when we try to manually assemble a shape with bricks, it takes a lot of time to choose the shape and color of the bricks, and the larger the thing we want to assemble, the longer it takes, wasting a lot of labor and time.
In order to solve these problems, this research first attempts to combine the network of encoder-decoder architecture with the construction of two-dimensional Lego flat bricks. Taking square bricks and bricks with rounded edges in Lego flat bricks as input, and through a given loss function, the existing photomosaic neural network research is extended from the collage problem to the combination problem of Lego flat bricks. At the same time, we also made a series of comparisons and analyzes on the input images and the generated images to show the effectiveness of this system.

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