Structured Light Depth Camera Motion Blur Detection and Deblurring

利用深度相機取得的三維場景的去模糊化在電腦視覺領域中是一個新穎的題目。動態模糊(motion blur)發生在許多基於結構光(structured light)的三維相機中。我們分析了基於結構光的三維相機產生動態模糊的原因，並設計了一個新穎的方法在三維場景中去模糊化。我們利用物體的模型去取代三維場景中有動態模糊的部分。因為我們處理連續的三維影像，因此我們可以在物體還沒產生動態模糊時建出物體的模型。我們的去模糊演算法分為兩個部分：動態模糊偵測以及動態模糊去模糊化。在動態模糊偵測部分，我們依物體的速度來辦定是否產生動態模糊。在動態模糊去模糊化部分，我們先判斷動態模糊的種類，並應用跌代最近點演算法(iterative closest point algorithm)針對不同種類的動態模糊來做不同的處理。我們對三組真實數據(real data)做實驗，成功得到了去模糊化的結果。Deblurring of 3D scenes captured by 3D sensors is a novel topic in computer vision. Motion blur occurs in a number of 3D sensors based on structured light techniques. We analyze the causes of motion blur captured by structured light depth cameras and design a novel algorithm using the speed cue and object models to deblur a 3D scene. The main idea is using the 3D model of an object to replace the blurry object in the scene. Because we aim to deal with consecutive 3D frame sequences, ie 3D videos, an object model can be built in the frame where the object is not blurry yet. Our deblurring method can be divided into two parts: motion blur detection and motion blur removal. For the motion blur detection part, we use the speed cue to detect where the motion blur is. For the motion blur removal part, first we judge the type of the motion blur, and then we apply the iterative closest point (ICP) algorithm in different ways according to the motion blur type. The proposed method is evaluated in real world cases and successfully accomplishes motion blur detection and blur removal