Structured Light Depth Camera Motion Blur Detection and Deblurring
Date Issued
2015
Date
2015
Author(s)
LU, HUNG-CHIH
Abstract
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.
Subjects
Depth Camera
Structured Light
Deblurring
Type
thesis
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