人類對於同一種類上的模型,有一種抽象的判斷標準。例如人類對於不同大
小、不同風格的椅子都能成功的認知為椅子,是由於對椅子有一個概念上的認
識。同樣的,人類對於其他物件也會有一個對應上的認知。然而,對於椅子概念
上的認識是什麼?當要具體化的描述時,就面臨了許多挑戰。例如四隻腳的椅
子、滾輪的椅子、單腳的椅子、一整體的椅子、長板凳的椅子等,這些物件人類
總是能知道是椅子,但是判斷的標準是什麼?

基於上述的知覺問題,椅子之所以是椅子是由於人類對於椅子有一個代表性
的認知,通常人類不會把香蕉歸類成一種椅子。然而每個人在對各種物件的代表
性認知與想法卻是不盡相同,因此我們限縮在於日常生活中容易見到的種類,並
進行三維模型的形狀特徵計算,以找出人類對於該物件的代表性判斷觀點。

本研究主要透過三維模型的特徵,如曲率、顯著點等等的幾何特徵;最後透
過機器學習來獲取到人類對於三維模型的判斷與預測,並探討三維模型的形狀特
徵對於人類知覺上的判斷的關聯性。

Human have an abstract cognition on a variety of 3D models. For example, it is easy for us to identify different kinds of chairs as chairs, because we know some concept about the chair. Similarly, for the other type of things we also have some concept to understand them. However, a question simple raises: what is the concept of a chair? It is very challenging when we try to describe it concretely. For instance, we know there are different kinds of chairs, like four-legs chair, swivel chair, stool, rocking chair, single-leg chair and so on. We all know these are chairs, even with different styles or weird structures, but what how do we make our judgment?

According to perception, we discovered that human generally do not treat a banana as a chair, and it is because we know some traits of a chair. But there are different view points from many people, so we just specify some categories that are commonly used in our life. We try to find out the human perspective through the feature computation and learning.

Our study uses different kinds of feature extracted from the 3D models, such as features of curvature, saliency and so on. After that we use machine learning to learn the views from some perceptual study. Finally we can predict and analyze the contribution of features.

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